Fuzzy inference neural network

Takatoshi Nishina, Masafumi Hagiwara

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

A new model for the design of Fuzzy Inference Neural Network (FINN) is proposed in this paper. It can automatically partition an input-output pattern space and can extract fuzzy if-then rules from numerical data. The proposed FINN is a two-layer network which utilizes Kohonen's algorithm. There are three learning phases: self-organizing learning phase, rule-extracting phase, and supervised learning phase. The FINN has the following distinctive features: (1) the membership functions of the premise part are constructed in the connection between the input layer and the rule layer, (2) it has an ability to select a suitable number of rules adaptively; and (3) It can extract more refined fuzzy if-then rules. We apply the proposed FINN to two illustrative examples, fuzzy control of an unmanned vehicle, and the prediction of the trend of stock prices. Computer simulation results indicate the effectiveness of the FINN.

Original languageEnglish
Pages (from-to)223-239
Number of pages17
JournalNeurocomputing
Volume14
Issue number3
DOIs
Publication statusPublished - 1997 Feb 28

Keywords

  • Fuzzy inference
  • Neural networks
  • Self-organization

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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