Neural Networks for Control

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W. Thomas Miller, Paul J. Werbos, Richard S. Sutton
MIT Press, 1995 - Computers - 524 pages

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.


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Overview of Designs and Capabilities
A Menu of Designs for Reinforcement Learning Over
Adaptive State Representation and Estimation Using
Adaptive Control using Neural Networks
A Summary Comparison of CMAC Neural Network
Recent Advances in Numerical Techniques for Large
First Results with Dyna An Integrated Architecture
Using Associative ContentAddressable Memories
An example of Self
An Adaptive Sensorimotor Network Inspired by
Some New Directions for Adaptive Control Theory
Applications of Neural Networks in Robotics
A Bioreactor Benchmark for Adaptive Network

VisionBased Robot Motion Planning

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About the author (1995)

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

PAUL JOHN WERBOS is a Program Director in the Engineering Directorate of the National Science Foundation as well as Past President of the International Neural Network Society. Previously, he developed and evaluated large-scale forecasting models at the Energy Information Administration of the Department of Energy, using backpropagation and other techniques discussed in this book. He has contributed, as a writer or editor, to several books on neural networks and has published more than forty journal articles and conference papers on a wide range of subjects.

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.

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