Neural Networks for Control | RDL Research Database

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 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 imprint

Keywords

MillerArtificial neural networkArtificial intelligenceControl (management)Environmental ethicsOperations researchCognitive scienceComputer sciencePhilosophyEngineeringPsychologyBiology

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Publication Info

Year: 1991
Type: book
Citations: 873
Access: Closed

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APA Style

                            
                            (1991). 
                            Neural Networks for Control. 
                            The MIT Press eBooks
                            
                            .
                            https://doi.org/10.7551/mitpress/4939.001.0001

Identifiers

DOI: 10.7551/mitpress/4939.001.0001

Data Quality

Data completeness: 68%