Synopsis
Neural Networks for Control highlights key issues in learning control and identifies research directions that could lead to practical solutions for control problems in critical application domains. It addresses general issues of neural network based control and neural network learning with regard to specific problems of motion planning and control in robotics, and takes up application domains well suited to the capabilities of neural network controllers. The appendix describes seven benchmark control problems.
W. Thomas Miller, III is Professor of Electrical and Computer Engineering at the University of New Hampshire. Richard S. Sutton works for GTE Laboratories Incorporated. Paul J. Werbos is Program Director for Neuroengineering at the National Science 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. Charles C. Jorgensen, C. Schley. Martin Herman, James S. Albus, Tsai-Hong Hong. Charles W. Anderson, W. Thomas Miller, III.
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About the Authors
W. Thomas Miller, III is Professor of Electrical and Computer Engineering at the University of New Hampshire.
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.
Paul J. Werbos is Program Director for Neuroengineering at the National Science Foundation.
Robert Hanna is Professor of Philosophy at the University of Colorado at Boulder. He is the author of Kant and the Foundations of Analytical Philosophy.
"About this title" may belong to another edition of this title.
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Neural Networks for Control
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Cambridge, Massachusetts, U.S.A.: Bradford Books, 1991
ISBN 10: 0262132613
ISBN 13: 9780262132619
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Neural Networks for Control (NEURAL NETWORK MODELLING AND CONNECTIONISM)
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