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 rule to approximate a numerical function from a set of examples of the functions 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 the 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
Publication statusPublished - Feb 1994
Externally publishedYes

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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