### Abstract

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 language | English |
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Pages (from-to) | 345-357 |

Number of pages | 13 |

Journal | Fuzzy Sets and Systems |

Volume | 71 |

Issue number | 3 |

DOIs | |

State | Published - 12 May 1995 |

### Keywords

- Fuzzy logic control
- Learning algorithms
- Neural networks

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## Cite this

*Fuzzy Sets and Systems*,

*71*(3), 345-357. https://doi.org/10.1016/0165-0114(94)00277-E