Fuzzy inference neural networks which automatically partition a pattern space and extract fuzzy if - then rules

Takatoshi Nishina, Masafumi Hagiwara, Masao Nakagawa

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

This paper proposes Fuzzy Inference Neural Networks (FINNs) which automatically partition a pattern space and extract fuzzy if-then rules from numerical data. There are three distinctive features in our model: 1) the membership functions of the fuzzified part are constructed in the connection between the input-part and the rule-layer; 2) Kohonen's self-organizing algorithm is applied to partition the input-output space. Consequently, they can extract polished fuzzy if-then rules; 3) they can adapt the number of rules automatically. We deal with two illustrative examples: 1) fuzzy control of unmanned vehicle; 2) prediction of the trend of stock prices. Computer simulation results indicate the effectiveness of the proposed FINNs.

Original languageEnglish
Pages1314-1319
Number of pages6
Publication statusPublished - 1994 Dec 1
EventProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3) - Orlando, FL, USA
Duration: 1994 Jun 261994 Jun 29

Other

OtherProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3)
CityOrlando, FL, USA
Period94/6/2694/6/29

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ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

Cite this

Nishina, T., Hagiwara, M., & Nakagawa, M. (1994). Fuzzy inference neural networks which automatically partition a pattern space and extract fuzzy if - then rules. 1314-1319. Paper presented at Proceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3), Orlando, FL, USA, .