Published 1993
| Published
Book Section - Chapter
Open
Learning Fuzzy Rule-Based Neural Networks for Control
- Creators
- Higgins, Charles M.
- Goodman, Rodney M.
Abstract
A three-step method for function approximation with a fuzzy system is proposed. First, the membership functions and an initial rule representation are learned; second, the rules are compressed as much as possible using information theory; and finally, a computational network is constructed to compute the function value. This system is applied to two control examples: learning the truck and trailer backer-upper control system, and learning a cruise control system for a radio-controlled model car.
Additional Information
© 1993 Morgan Kaufmann. This work was supported in part by Pacific Bell, and in part by DARPA and ONR under grant no. N00014-92-J-1860.Attached Files
Published - 649-learning-fuzzy-rule-based-neural-networks-for-control.pdf
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649-learning-fuzzy-rule-based-neural-networks-for-control.pdf
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Additional details
- Eprint ID
- 64217
- Resolver ID
- CaltechAUTHORS:20160203-163952250
- Pacific Bell
- Defense Advanced Research Projects Agency (DARPA)
- Office of Naval Research (ONR)
- N00014-92-J-1860
- Created
-
2016-02-04Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Series Name
- Advances in Neural Information Processing Systems
- Series Volume or Issue Number
- 5