Neural Networks for Control

Front Cover
W. Thomas Miller, Paul J. Werbos, Richard S. Sutton
MIT Press, 1995 - Psychology - 544 pages
0 Reviews

Neural Networks for Control highlights key issues in learning control and identifiesresearch directions that could lead to practical solutions for control problems in criticalapplication domains. It addresses general issues of neural network based control and neural networklearning with regard to specific problems of motion planning and control in robotics, and takes upapplication domains well suited to the capabilities of neural network controllers. The appendixdescribes seven benchmark control problems.W. Thomas Miller, III is Professor of Electrical andComputer Engineering at the University of New Hampshire. Richard S. Sutton works for GTELaboratories Incorporated. Paul J. Werbos is Program Director for Neuroengineering at the NationalScience Foundation.Contributors: Andrew G. Barto. Ronald J. Williams. Paul J. Werbos. Kumpati S.Narendra. L. Gordon Kraft, III, David P. Campagna. Mitsuo Kawato. Bartlett W. Met. Christopher G.Atkeson, David J. Reinkensmeyer. Derrick Nguyen, Bernard Widrow. James C. Houk, Satinder P. Singh,Charles Fisher. Judy A. Franklin, Oliver G. Selfridge. Arthur C. Sanderson. Lyle H. Ungar. CharlesC. Jorgensen, C. Schley. Martin Herman, James S. Albus, Tsai-Hong Hong. Charles W. Anderson, W.Thomas Miller, III.

 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Overview of Designs and Capabilities
59
A Menu of Designs for Reinforcement Learning Over
67
Adaptive State Representation and Estimation Using
97
Adaptive Control using Neural Networks
115
A Summary Comparison of CMAC Neural Network
143
Recent Advances in Numerical Techniques for Large
171
First Results with Dyna An Integrated Architecture
179
MOTION CONTROL
191
Using Associative ContentAddressable Memories
255
An example of Self
287
An Adaptive Sensorimotor Network Inspired by
301
Some New Directions for Adaptive Control Theory
349
Applications of Neural Networks in Robotics
365
A Bioreactor Benchmark for Adaptive Network
387
Index
511
Copyright

VisionBased Robot Motion Planning
229

Common terms and phrases

action activity adaptive control adjusted algorithm applications approach associative assumed backpropagation Barto calculate called chapter command complex computation connectionist connections convergence critic derivatives described desired determine developed direct discussed dynamic effective engineering equation error estimation evaluation example experiences fiber Figure function given identification IEEE implemented important initial input inverse kind layer learning linear mapping measure memory methods motor movement neural network noise nonlinear objective obstacles operations optimal output parallel parameter parameter estimation pattern performance planning plant position possible present problem programming Purkinje cells recurrent reference reinforcement represent representation response robot rule Science sensor shown in figure shows signal simple simulation space specified step structure studied task techniques theory tion trajectory units University values variables vector vehicle weights Werbos

References to this book

About the author (1995)

W. Thomas Miller, III is Professor of Electrical and Computer Engineering at the University of New Hampshire.

Paul J. Werbos is Program Director for Neuroengineering at the National Science Foundation.

Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind.

Bibliographic information

QR code for Neural Networks for Control
TitleNeural Networks for Control
A Bradford book
Neural network modeling and connectionism
EditorsW. Thomas Miller, Paul J. Werbos, Richard S. Sutton
Editionillustrated, reprint, revised
PublisherMIT Press, 1995
ISBN026263161X, 9780262631617
Length544 pages
Subjects › 

Psychology / Cognitive Psychology & Cognition
  
Export CitationBiBTeX EndNote RefMan