Abstract: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...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 Bradford Books imprintRead More
Publication Year: 1991
Publication Date: 1991-01-10
Language: en
Type: book
Indexed In: ['crossref']
Access and Citation
Cited By Count: 875
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot