Neural Networks for Control

Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems. A Bradford Book. Neural Network Modeling and Connectionism series
 
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.

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