Fuzzy Rule-Based Networks for Control

Charles M. Higgins, Rodney M. Goodman

Research output: Contribution to journalArticle

38 Scopus citations

Abstract

We present a method for learning fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: first, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an information-theoretic approach for induction of rules from discrete-valued data; and, finally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and trailer backer-upper control system.

Original languageEnglish (US)
Pages (from-to)82-88
Number of pages7
JournalIEEE Transactions on Fuzzy Systems
Volume2
Issue number1
DOIs
StatePublished - Feb 1994

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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