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14.451 Dynamic Optimization Methods with Applications (MIT) 14.451 Dynamic Optimization Methods with Applications (MIT)

Description

This course focuses on dynamic optimization methods, both in discrete and in continuous time. We approach these problems from a dynamic programming and optimal control perspective. We also study the dynamic systems that come from the solutions to these problems. The course will illustrate how these techniques are useful in various applications, drawing on many economic examples. However, the focus will remain on gaining a general command of the tools so that they can be applied later in other classes. This course focuses on dynamic optimization methods, both in discrete and in continuous time. We approach these problems from a dynamic programming and optimal control perspective. We also study the dynamic systems that come from the solutions to these problems. The course will illustrate how these techniques are useful in various applications, drawing on many economic examples. However, the focus will remain on gaining a general command of the tools so that they can be applied later in other classes.

Subjects

vector spaces | vector spaces | principle of optimality | principle of optimality | concavity of the value function | concavity of the value function | differentiability of the value function | differentiability of the value function | Euler equations | Euler equations | deterministic dynamics | deterministic dynamics | models with constant returns to scale | models with constant returns to scale | nonstationary models | nonstationary models | stochastic dynamic programming | stochastic dynamic programming | stochastic Euler equations | stochastic Euler equations | stochastic dynamics | stochastic dynamics | calculus of variations | calculus of variations | the maximum principle | the maximum principle | discounted infinite-horizon optimal control | discounted infinite-horizon optimal control | saddle-path stability | saddle-path stability

License

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6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT)

Description

This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages (finite and infinite horizon). We will also discuss some approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations. This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages (finite and infinite horizon). We will also discuss some approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.

Subjects

dynamic programming | dynamic programming | stochastic control | stochastic control | decision making | decision making | uncertainty | uncertainty | sequential decision making | sequential decision making | finite horizon | finite horizon | infinite horizon | infinite horizon | approximation methods | approximation methods | state space | state space | large state space | large state space | optimal control | optimal control | dynamical system | dynamical system | dynamic programming and optimal control | dynamic programming and optimal control | deterministic systems | deterministic systems | shortest path | shortest path | state information | state information | rollout | rollout | stochastic shortest path | stochastic shortest path | approximate dynamic programming | approximate dynamic programming

License

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

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6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT)

Description

This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). Approximation methods for problems involving large state spaces are also presented and discussed. This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). Approximation methods for problems involving large state spaces are also presented and discussed.

Subjects

dynamic programming | dynamic programming | | stochastic control | | stochastic control | | mathematics | optimization | | | mathematics | optimization | | algorithms | | algorithms | | probability | | probability | | Markov chains | | Markov chains | | optimal control | optimal control | stochastic control | stochastic control | mathematics | mathematics | optimization | optimization | algorithms | algorithms | probability | probability | Markov chains | Markov chains

License

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT)

Description

The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations. The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.

Subjects

dynamic programming | dynamic programming | stochastic control | stochastic control | algorithms | algorithms | finite-state | finite-state | continuous-time | continuous-time | imperfect state information | imperfect state information | suboptimal control | suboptimal control | finite horizon | finite horizon | infinite horizon | infinite horizon | discounted problems | discounted problems | stochastic shortest path | stochastic shortest path | approximate dynamic programming | approximate dynamic programming

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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15.099 Readings in Optimization (MIT) 15.099 Readings in Optimization (MIT)

Description

In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention. In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention.

Subjects

deterministic optimization; algorithms; stochastic processes; random number generation; simplex method; nonlinear; convex; complexity analysis; semidefinite programming; heuristic; global optimization; Las Vegas algorithm; randomized algorithm; linear programming; search techniques; hit and run; NP-hard; approximation | deterministic optimization; algorithms; stochastic processes; random number generation; simplex method; nonlinear; convex; complexity analysis; semidefinite programming; heuristic; global optimization; Las Vegas algorithm; randomized algorithm; linear programming; search techniques; hit and run; NP-hard; approximation | deterministic optimization | deterministic optimization | algorithms | algorithms | stochastic processes | stochastic processes | random number generation | random number generation | simplex method | simplex method | nonlinear | nonlinear | convex | convex | complexity analysis | complexity analysis | semidefinite programming | semidefinite programming | heuristic | heuristic | global optimization | global optimization | Las Vegas algorithm | Las Vegas algorithm | randomized algorithm | randomized algorithm | linear programming | linear programming | search techniques | search techniques | hit and run | hit and run | NP-hard | NP-hard | approximation | approximation

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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15.070J Advanced Stochastic Processes (MIT) 15.070J Advanced Stochastic Processes (MIT)

Description

This class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models. This class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models.

Subjects

analysis | analysis | modeling | modeling | stochastic processes | stochastic processes | theoretic probability | theoretic probability | martingales | martingales | filtration | filtration | stopping theorems | stopping theorems | large deviations theory | large deviations theory | Brownian motion | Brownian motion | reflected Brownian motion | reflected Brownian motion | stochastic integration | stochastic integration | Ito calculus | Ito calculus | functional limit theorems | functional limit theorems | applications | applications | finance theory | finance theory | insurance | insurance | queueing | queueing | inventory models | inventory models

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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15.070 Advanced Stochastic Processes (MIT) 15.070 Advanced Stochastic Processes (MIT)

Description

The class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models. The class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models.

Subjects

analysis | analysis | modeling | modeling | stochastic processes | stochastic processes | theoretic probability | theoretic probability | martingales | martingales | filtration | filtration | stopping theorems | stopping theorems | large deviations theory | large deviations theory | Brownian motion | Brownian motion | reflected Brownian motion | reflected Brownian motion | stochastic integration | stochastic integration | Ito calculus | Ito calculus | functional limit theorems | functional limit theorems | applications | applications | finance theory | finance theory | insurance | insurance | queueing | queueing | inventory models | inventory models

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT)

Description

Includes audio/video content: AV special element video. The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations. Includes audio/video content: AV special element video. The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.

Subjects

dynamic programming | dynamic programming | stochastic control | stochastic control | algorithms | algorithms | finite-state | finite-state | continuous-time | continuous-time | imperfect state information | imperfect state information | suboptimal control | suboptimal control | finite horizon | finite horizon | infinite horizon | infinite horizon | discounted problems | discounted problems | stochastic shortest path | stochastic shortest path | approximate dynamic programming | approximate dynamic programming

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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14.451 Dynamic Optimization Methods with Applications (MIT)

Description

This course focuses on dynamic optimization methods, both in discrete and in continuous time. We approach these problems from a dynamic programming and optimal control perspective. We also study the dynamic systems that come from the solutions to these problems. The course will illustrate how these techniques are useful in various applications, drawing on many economic examples. However, the focus will remain on gaining a general command of the tools so that they can be applied later in other classes.

Subjects

vector spaces | principle of optimality | concavity of the value function | differentiability of the value function | Euler equations | deterministic dynamics | models with constant returns to scale | nonstationary models | stochastic dynamic programming | stochastic Euler equations | stochastic dynamics | calculus of variations | the maximum principle | discounted infinite-horizon optimal control | saddle-path stability

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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14.123 Microeconomic Theory III (MIT) 14.123 Microeconomic Theory III (MIT)

Description

This half-semester course discusses decision theory and topics in game theory. We present models of individual decision-making under certainty and uncertainty. Topics include preference orderings, expected utility, risk, stochastic dominance, supermodularity, monotone comparative statics, background risk, game theory, rationalizability, iterated strict dominance multi-stage games, sequential equilibrium, trembling-hand perfection, stability, signaling games, theory of auctions, global games, repeated games, and correlation. This half-semester course discusses decision theory and topics in game theory. We present models of individual decision-making under certainty and uncertainty. Topics include preference orderings, expected utility, risk, stochastic dominance, supermodularity, monotone comparative statics, background risk, game theory, rationalizability, iterated strict dominance multi-stage games, sequential equilibrium, trembling-hand perfection, stability, signaling games, theory of auctions, global games, repeated games, and correlation.

Subjects

microeconomics | microeconomics | microeconomic theory | microeconomic theory | preference | preference | utility representation | utility representation | expected utility | expected utility | positive interpretation | positive interpretation | normative interpretation | normative interpretation | risk | risk | stochastic dominance | stochastic dominance | insurance | insurance | finance | finance | supermodularity | supermodularity | comparative statics | comparative statics | decision theory | decision theory | game theory | game theory | rationalizability | rationalizability | iterated strict dominance | iterated strict dominance | iterated conditional dominance | iterated conditional dominance | bargaining | bargaining | equilibrium | equilibrium | sequential equilibrium | sequential equilibrium | trembling-hand perfection | trembling-hand perfection | signaling games | signaling games | auctions | auctions | global games | global games | repeated games | repeated games | correlation | correlation

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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14.384 Time Series Analysis (MIT) 14.384 Time Series Analysis (MIT)

Description

Subjects

univariate stationary | univariate stationary | univariate non-stationary | univariate non-stationary | vector autoregressions | vector autoregressions | frequency domain analysis | frequency domain analysis | persistent time series | persistent time series | structural breaks | structural breaks | dynamic stochastic general equilibrium | dynamic stochastic general equilibrium | DSGE | DSGE | Bayesian | Bayesian | econometrics | econometrics | VAR | VAR | unit root | unit root | prediction regression | prediction regression | GMM | GMM | MCMC | MCMC

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.323 Principles of Optimal Control (MIT) 16.323 Principles of Optimal Control (MIT)

Description

This course studies the principles of deterministic optimal control. It uses variational calculus and Pontryagin's maximum principle. It focuses on applications of the theory, including optimal feedback control, time-optimal control, and others. Dynamic programming and numerical search algorithms are introduced briefly. This course studies the principles of deterministic optimal control. It uses variational calculus and Pontryagin's maximum principle. It focuses on applications of the theory, including optimal feedback control, time-optimal control, and others. Dynamic programming and numerical search algorithms are introduced briefly.

Subjects

nonlinear optimization | nonlinear optimization | linear quadratic regulators | linear quadratic regulators | MATLAB implementation | MATLAB implementation | dynamic programming | dynamic programming | calculus of variations | calculus of variations | LQR | LQR | LQG | LQG | stochastic optimization | stochastic optimization | on-line optimization and control | on-line optimization and control | constrained optimization | constrained optimization | signals | signals | system norms | system norms | Model Predictive Behavior | Model Predictive Behavior | quadratic programming | quadratic programming | mixed-integer linear programming | mixed-integer linear programming | linear programming | linear programming

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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12.540 Principles of the Global Positioning System (MIT) 12.540 Principles of the Global Positioning System (MIT)

Description

The aim of this course is to introduce the principles of the Global Positioning System and to demonstrate its application to various aspects of Earth Sciences. The specific content of the course depends each year on the interests of the students in the class. In some cases, the class interests are towards the geophysical applications of GPS and we concentrate on high precision(millimeter level) positioning on regional and global scales. In other cases, the interests have been more toward engineering applications of kinematic positioning with GPS in which case the concentration is on positioning with slightly less accuracy but being able to do so for a moving object. In all cases, we concentrate on the fundamen The aim of this course is to introduce the principles of the Global Positioning System and to demonstrate its application to various aspects of Earth Sciences. The specific content of the course depends each year on the interests of the students in the class. In some cases, the class interests are towards the geophysical applications of GPS and we concentrate on high precision(millimeter level) positioning on regional and global scales. In other cases, the interests have been more toward engineering applications of kinematic positioning with GPS in which case the concentration is on positioning with slightly less accuracy but being able to do so for a moving object. In all cases, we concentrate on the fundamen

Subjects

Global Positioning System | Global Positioning System | Earth Sciences | Earth Sciences | geophysical applications | geophysical applications | GPS | GPS | engineering applications | engineering applications | kinematic positioning | kinematic positioning | precision | precision | accuracy | accuracy | moving objects | moving objects | coordinate | coordinate | time | time | systems | systems | satellite | satellite | geodetic | geodetic | orbital | orbital | motions | motions | pseudo ranges | pseudo ranges | carrier phases | carrier phases | stochastic | stochastic | mathematics | mathematics | models | models | data | data | analysis | analysis | estimation | estimation

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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12.215 Modern Navigation (MIT) 12.215 Modern Navigation (MIT)

Description

The development of the Global Positioning System (GPS) started in the 1960s, and the system became operational in 1992. The system has seen many diverse applications develop in the last few years with the accuracy of positioning ranging from 100 meters (the civilian restricted accuracy requirement) to 1 millimeter (without the need for a security clearance!) In this course we will apply many of basic principles of science and mathematics learnt at MIT to explore the applications and principles of GPS. We also use GPS and other equipment in the class (and outside on Campus) to demonstrate the uses of this system.Technical RequirementsAny number of development tools can be used to compile and run the .f files found on this course site. Please refer to the The development of the Global Positioning System (GPS) started in the 1960s, and the system became operational in 1992. The system has seen many diverse applications develop in the last few years with the accuracy of positioning ranging from 100 meters (the civilian restricted accuracy requirement) to 1 millimeter (without the need for a security clearance!) In this course we will apply many of basic principles of science and mathematics learnt at MIT to explore the applications and principles of GPS. We also use GPS and other equipment in the class (and outside on Campus) to demonstrate the uses of this system.Technical RequirementsAny number of development tools can be used to compile and run the .f files found on this course site. Please refer to the

Subjects

Global Positioning | Global Positioning | Global Positioning System | Global Positioning System | GPScivilian restricted accuracy requirment | GPScivilian restricted accuracy requirment | basic principles | basic principles | science | science | mathematics | mathematics | GPS | GPS | navigation | navigation | accuracy | accuracy | civilian | civilian | application | application | coordinate systems | coordinate systems | lattitude | lattitude | longitude | longitude | deformable | deformable | Earth | Earth | estimation | estimation | aircraft | aircraft | stochastic | stochastic | mathematical | mathematical | models | models | statistics | statistics | dynamic systems | dynamic systems | pseudorange | pseudorange | phase measurements | phase measurements | celestial | celestial | sattelite | sattelite | astronomical observations | astronomical observations | radio | radio | ship | ship | automobile | automobile

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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2.23 Hydrofoils and Propellers (13.04) (MIT) 2.23 Hydrofoils and Propellers (13.04) (MIT)

Description

This course deals with theory and design of hydrofoil sections; lifting and thickness problems for sub-cavitating sections, unsteady flow problems. It focuses on computer-aided design of low drag, cavitation free sections. The course also covers lifting line and lifting surface theory with applications to hydrofoil craft, rudder, and control surface design. Topics include propeller lifting line and lifting surface theory; computer-aided design of wake adapted propellers, unsteady propeller thrust and torque. The course is also an introduction to subjects like flow about axially symmetric bodies and low-aspect ratio lifting surfaces, and hydrodynamic performance and design of waterjets. We will also do an analysis of performance and design of wind turbine rotors in steady and stochastic win This course deals with theory and design of hydrofoil sections; lifting and thickness problems for sub-cavitating sections, unsteady flow problems. It focuses on computer-aided design of low drag, cavitation free sections. The course also covers lifting line and lifting surface theory with applications to hydrofoil craft, rudder, and control surface design. Topics include propeller lifting line and lifting surface theory; computer-aided design of wake adapted propellers, unsteady propeller thrust and torque. The course is also an introduction to subjects like flow about axially symmetric bodies and low-aspect ratio lifting surfaces, and hydrodynamic performance and design of waterjets. We will also do an analysis of performance and design of wind turbine rotors in steady and stochastic win

Subjects

Theory and design of hydrofoil sections | Theory and design of hydrofoil sections | lifting and thickness problems | lifting and thickness problems | sub-cavitating sections | sub-cavitating sections | unsteady flow problems | unsteady flow problems | computer-aided design | computer-aided design | low drag | low drag | cavitation free sections | cavitation free sections | Lifting line and lifting surface theory | Lifting line and lifting surface theory | hydrofoil craft | hydrofoil craft | rudder | rudder | and control surface design | and control surface design | propeller lifting line | propeller lifting line | lifting surface theory | lifting surface theory | wake adapted propellers | wake adapted propellers | unsteady propeller thrust and torque | unsteady propeller thrust and torque | axially symmetric bodies | axially symmetric bodies | low-aspect ratio lifting surfaces | low-aspect ratio lifting surfaces | Hydrodynamic performance | Hydrodynamic performance | design of waterjets | design of waterjets | wind turbine rotors in steady and stochastic wind | wind turbine rotors in steady and stochastic wind | hydrofoil craft | rudder | and control surface design | hydrofoil craft | rudder | and control surface design | 9. low drag | cavitation free sections | 9. low drag | cavitation free sections | 5. hydrofoil craft | rudder | and control surface design | 5. hydrofoil craft | rudder | and control surface design | low drag | cavitation free sections | low drag | cavitation free sections

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13.04 Hydrofoils and Propellers (MIT) 13.04 Hydrofoils and Propellers (MIT)

Description

This course deals with theory and design of hydrofoil sections; lifting and thickness problems for sub-cavitating sections, unsteady flow problems. It focuses on computer-aided design of low drag, cavitation free sections. The course also covers lifting line and lifting surface theory with applications to hydrofoil craft, rudder, and control surface design. Topics include propeller lifting line and lifting surface theory; computer-aided design of wake adapted propellers, unsteady propeller thrust and torque. The course is also an introduction to subjects like flow about axially symmetric bodies and low-aspect ratio lifting surfaces, and hydrodynamic performance and design of waterjets. We will also do an analysis of performance and design of wind turbine rotors in steady and stochastic win This course deals with theory and design of hydrofoil sections; lifting and thickness problems for sub-cavitating sections, unsteady flow problems. It focuses on computer-aided design of low drag, cavitation free sections. The course also covers lifting line and lifting surface theory with applications to hydrofoil craft, rudder, and control surface design. Topics include propeller lifting line and lifting surface theory; computer-aided design of wake adapted propellers, unsteady propeller thrust and torque. The course is also an introduction to subjects like flow about axially symmetric bodies and low-aspect ratio lifting surfaces, and hydrodynamic performance and design of waterjets. We will also do an analysis of performance and design of wind turbine rotors in steady and stochastic win

Subjects

Theory and design of hydrofoil sections | Theory and design of hydrofoil sections | lifting and thickness problems | lifting and thickness problems | sub-cavitating sections | sub-cavitating sections | unsteady flow problems | unsteady flow problems | computer-aided design | computer-aided design | low drag | low drag | cavitation free sections | cavitation free sections | Lifting line and lifting surface theory | Lifting line and lifting surface theory | hydrofoil craft | hydrofoil craft | rudder | rudder | and control surface design | and control surface design | propeller lifting line | propeller lifting line | lifting surface theory | lifting surface theory | wake adapted propellers | wake adapted propellers | unsteady propeller thrust and torque | unsteady propeller thrust and torque | axially symmetric bodies | axially symmetric bodies | low-aspect ratio lifting surfaces | low-aspect ratio lifting surfaces | Hydrodynamic performance | Hydrodynamic performance | design of waterjets | design of waterjets | wind turbine rotors in steady and stochastic wind | wind turbine rotors in steady and stochastic wind | hydrofoil craft | rudder | and control surface design | hydrofoil craft | rudder | and control surface design | 9. low drag | cavitation free sections | 9. low drag | cavitation free sections | 5. hydrofoil craft | rudder | and control surface design | 5. hydrofoil craft | rudder | and control surface design | low drag | cavitation free sections | low drag | cavitation free sections | 2.23 | 2.23

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9.29J Introduction to Computational Neuroscience (MIT) 9.29J Introduction to Computational Neuroscience (MIT)

Description

Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, Fourier analysis, signal detection theory, probability theory, and information theory. Applications to neural coding, focusing on the visual system. Hodgkin-Huxley and related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission. Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, Fourier analysis, signal detection theory, probability theory, and information theory. Applications to neural coding, focusing on the visual system. Hodgkin-Huxley and related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.

Subjects

neural coding | neural coding | dynamics | dynamics | convolution | convolution | correlation | correlation | linear systems | linear systems | Fourier analysis | Fourier analysis | signal detection theory | signal detection theory | probability theory | probability theory | information theory | information theory | neural excitability | neural excitability | stochastic models | stochastic models | ion channels | ion channels | cable theory | cable theory | 9.29 | 9.29 | 8.261 | 8.261

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6.832 Underactuated Robotics (MIT) 6.832 Underactuated Robotics (MIT)

Description

Includes audio/video content: AV lectures. Robots today move far too conservatively, using control systems that attempt to maintain full control authority at all times. Humans and animals move much more aggressively by routinely executing motions which involve a loss of instantaneous control authority. Controlling nonlinear systems without complete control authority requires methods that can reason about and exploit the natural dynamics of our machines. This course discusses nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on machine learning methods. Topics include nonlinear dynamics of passive robots (walkers, swimmers, flyers), motion planning, partial feedback linearization, energy-shaping control, analytical optimal control, reinforcement learning/a Includes audio/video content: AV lectures. Robots today move far too conservatively, using control systems that attempt to maintain full control authority at all times. Humans and animals move much more aggressively by routinely executing motions which involve a loss of instantaneous control authority. Controlling nonlinear systems without complete control authority requires methods that can reason about and exploit the natural dynamics of our machines. This course discusses nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on machine learning methods. Topics include nonlinear dynamics of passive robots (walkers, swimmers, flyers), motion planning, partial feedback linearization, energy-shaping control, analytical optimal control, reinforcement learning/a

Subjects

underactuated robotics | underactuated robotics | actuated systems | actuated systems | nonlinear dynamics | nonlinear dynamics | simple pendulum | simple pendulum | optimal control | optimal control | double integrator | double integrator | quadratic regulator | quadratic regulator | Hamilton-Jacobi-Bellman sufficiency | Hamilton-Jacobi-Bellman sufficiency | minimum time control | minimum time control | acrobot | acrobot | cart-pole | cart-pole | partial feedback linearization | partial feedback linearization | energy shaping | energy shaping | policy search | policy search | open-loop optimal control | open-loop optimal control | trajectory stabilization | trajectory stabilization | iterative linear quadratic regulator | iterative linear quadratic regulator | differential dynamic programming | differential dynamic programming | walking models | walking models | rimless wheel | rimless wheel | compass gait | compass gait | kneed compass gait | kneed compass gait | feedback control | feedback control | running models | running models | spring-loaded inverted pendulum | spring-loaded inverted pendulum | Raibert hoppers | Raibert hoppers | motion planning | motion planning | randomized motion planning | randomized motion planning | rapidly-exploring randomized trees | rapidly-exploring randomized trees | probabilistic road maps | probabilistic road maps | feedback motion planning | feedback motion planning | planning with funnels | planning with funnels | linear quadratic regulator | linear quadratic regulator | function approximation | function approximation | state distribution dynamics | state distribution dynamics | state estimation | state estimation | stochastic optimal control | stochastic optimal control | aircraft | aircraft | swimming | swimming | flapping flight | flapping flight | randomized policy gradient | randomized policy gradient | model-free value methods | model-free value methods | temporarl difference learning | temporarl difference learning | Q-learning | Q-learning | actor-critic methods | actor-critic methods

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6.345 Automatic Speech Recognition (MIT) 6.345 Automatic Speech Recognition (MIT)

Description

Includes audio/video content: AV special element audio. 6.345 introduces students to the rapidly developing field of automatic speech recognition. Its content is divided into three parts. Part I deals with background material in the acoustic theory of speech production, acoustic-phonetics, and signal representation. Part II describes algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques. Part III compares and contrasts the various approaches to speech recognition, and describes advanced techniques used for acoustic-phonetic modelling, robust speech recognition, speaker adaptation, processing paralinguistic information, speech understanding, and multimodal processing. Includes audio/video content: AV special element audio. 6.345 introduces students to the rapidly developing field of automatic speech recognition. Its content is divided into three parts. Part I deals with background material in the acoustic theory of speech production, acoustic-phonetics, and signal representation. Part II describes algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques. Part III compares and contrasts the various approaches to speech recognition, and describes advanced techniques used for acoustic-phonetic modelling, robust speech recognition, speaker adaptation, processing paralinguistic information, speech understanding, and multimodal processing.

Subjects

speech recognition | speech recognition | automatic speech recognition | automatic speech recognition | acoustic theory | acoustic theory | speech production | speech production | acoustic-phonetics | acoustic-phonetics | signal representation | signal representation | pattern classification | pattern classification | search algorithms | search algorithms | stochastic modelling | stochastic modelling | language modelling | language modelling | speaker adaptation | speaker adaptation | paralinguistic information | paralinguistic information | speech understanding | speech understanding | multimodal processing | multimodal processing

License

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2.717J Optical Engineering (MIT) 2.717J Optical Engineering (MIT)

Description

This course concerns the theory and practice of optical methods in engineering and system design, with an emphasis on diffraction, statistical optics, holography, and imaging. It provides the engineering methodology skills necessary to incorporate optical components in systems serving diverse areas such as precision engineering and metrology, bio-imaging, and computing (sensors, data storage, communication in multi-processor systems). Experimental demonstrations and a design project are included. This course concerns the theory and practice of optical methods in engineering and system design, with an emphasis on diffraction, statistical optics, holography, and imaging. It provides the engineering methodology skills necessary to incorporate optical components in systems serving diverse areas such as precision engineering and metrology, bio-imaging, and computing (sensors, data storage, communication in multi-processor systems). Experimental demonstrations and a design project are included.

Subjects

optical methods in engineering and system design | optical methods in engineering and system design | diffraction | statistical optics | holography | and imaging | diffraction | statistical optics | holography | and imaging | Statistical Optics | Inverse Problems (i.e. theory of imaging) | Statistical Optics | Inverse Problems (i.e. theory of imaging) | applications in precision engineering and metrology | bio-imaging | and computing (sensors | data storage | communication in multi-processor systems) | applications in precision engineering and metrology | bio-imaging | and computing (sensors | data storage | communication in multi-processor systems) | Fourier optics | Fourier optics | probability | probability | stochastic processes | stochastic processes | light statistics | light statistics | theory of light coherence | theory of light coherence | van Cittert-Zernicke Theorem | van Cittert-Zernicke Theorem | statistical optics applications | statistical optics applications | inverse problems | inverse problems | information-theoretic views | information-theoretic views | information theory | information theory | 2.717 | 2.717 | MAS.857 | MAS.857

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2.997 Decision Making in Large Scale Systems (MIT) 2.997 Decision Making in Large Scale Systems (MIT)

Description

This course is an introduction to the theory and application of large-scale dynamic programming. Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks. This course is an introduction to the theory and application of large-scale dynamic programming. Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks.

Subjects

algorithm | algorithm | markov decision process | markov decision process | dynamic programming | dynamic programming | stochastic models | stochastic models | policy iteration | policy iteration | Q-Learning | Q-Learning | reinforcement learning | reinforcement learning | Lyapunov function | Lyapunov function | ODE | ODE | TD-Learning | TD-Learning | value function approximation | value function approximation | linear programming | linear programming | policy search | policy search | policy gradient | policy gradient | actor-critic | actor-critic | experts algorithm | experts algorithm | regret minimization and calibration | regret minimization and calibration | games. | games.

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2.008 Design and Manufacturing II (MIT) 2.008 Design and Manufacturing II (MIT)

Description

Integration of design, engineering, and management disciplines and practices for analysis and design of manufacturing enterprises. Emphasis is on the physics and stochastic nature of manufacturing processes and systems, and their effects on quality, rate, cost, and flexibility. Topics include process physics and control, design for manufacturing, and manufacturing systems. Group project requires design and fabrication of parts using mass-production and assembly methods to produce a product in quantity. Integration of design, engineering, and management disciplines and practices for analysis and design of manufacturing enterprises. Emphasis is on the physics and stochastic nature of manufacturing processes and systems, and their effects on quality, rate, cost, and flexibility. Topics include process physics and control, design for manufacturing, and manufacturing systems. Group project requires design and fabrication of parts using mass-production and assembly methods to produce a product in quantity.

Subjects

manufacturing enterprises | manufacturing enterprises | physics | physics | stochastic nature of manufacturing processes | stochastic nature of manufacturing processes | quality | quality | rate | rate | cost | cost | flexibility | flexibility | process physics | process physics | process control | process control

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6.011 Introduction to Communication, Control, and Signal Processing (MIT) 6.011 Introduction to Communication, Control, and Signal Processing (MIT)

Description

This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection. This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.

Subjects

Input-output | Input-output | state-space models | state-space models | linear systems | linear systems | deterministic and random signals | deterministic and random signals | time- and transform-domain representations | time- and transform-domain representations | sampling | sampling | discrete-time processing | discrete-time processing | continuous-time signals | continuous-time signals | state feedback | state feedback | observers | observers | probabilistic models | probabilistic models | stochastic processes | stochastic processes | correlation functions | correlation functions | power spectra | power spectra | whitening filters | whitening filters | Detection | Detection | matched filters | matched filters | Least-mean square error estimation | Least-mean square error estimation | Wiener filtering | Wiener filtering

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