@inproceedings{cd3f13d1fd1a4356a6f8ec57b00b42bb,
title = "Learning Fuzzy Rule-Based Neural Networks for Control",
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.",
author = "Higgins, \{Charles M.\} and Goodman, \{Rodney M.\}",
note = "Publisher Copyright: {\textcopyright} 1992 Neural information processing systems foundation. All rights reserved.; 5th Advances in Neural Information Processing Systems, NIPS 1992 ; Conference date: 30-11-1992 Through 03-12-1992",
year = "1992",
doi = "10.5555/645753.667894",
language = "English (US)",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
pages = "350--357",
editor = "Hanson, \{Stephen Jose\} and Cowan, \{Jack D.\} and Giles, \{C. Lee\}",
booktitle = "Advances in Neural Information Processing Systems 5, NIPS 1992",
}