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Learning Fuzzy Rule-Based Neural Networks for Control

Higgins, Charles M. and Goodman, Rodney M. (1993) Learning Fuzzy Rule-Based Neural Networks for Control. In: Advances in Neural Information Processing Systems 5 (NIPS 1992). Advances in Neural Information Processing Systems. No.5. Morgan Kaufmann , San Mateo, CA, pp. 350-357. ISBN 1-55860-274-7. https://resolver.caltech.edu/CaltechAUTHORS:20160203-163952250

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://papers.nips.cc/paper/649-learning-fuzzy-rule-based-neural-networks-for-controlOrganizationArticle
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.
Funders:
Funding AgencyGrant Number
Pacific BellUNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Office of Naval Research (ONR)N00014-92-J-1860
Series Name:Advances in Neural Information Processing Systems
Issue or Number:5
Record Number:CaltechAUTHORS:20160203-163952250
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160203-163952250
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:64217
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:04 Feb 2016 01:10
Last Modified:03 Oct 2019 09:35

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