A fuzzy neural network for rule acquiring on fuzzy control systems

Jyh-Jiun Shann*, H. C. Fu

*Corresponding author for this work

Research output: Contribution to journalArticle

86 Scopus citations


This paper presents a layer-structured fuzzy neural network (FNN) for learning rules of fuzzy-logic control systems. Initially, FNN is constructed to contain all the possible fuzzy rules. We propose a two-phase learning procedure for this network. The first phase is a error-backprop (EBP) training, and the second phase is a rule pruning. Since some functions of the nodes in the FNN have the competitive characteristics, the EBP training will converge quickly. After the training, a pruning process is performed to delete redundant rules for obtaining a concise fuzzy rule base. Simulation results show that for the truck backer-upper control problem, the training phase learns the knowledge of fuzzy rules in several dozen epochs with an error rate of less than 1%. Moreover, the fuzzy rule base generated by the pruning process contains only 14% of the initial fuzzy rules and is identical to the target fuzzy rule base.

Original languageEnglish
Pages (from-to)345-357
Number of pages13
JournalFuzzy Sets and Systems
Issue number3
StatePublished - 12 May 1995


  • Fuzzy logic control
  • Learning algorithms
  • Neural networks

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