Fuzzy inference neural networks which automatically partition a pattern space and extract fuzzy if-then rules
Takatoshi Nishina, Masafumi Hagiwara, Masaki Nakagawa · 1994
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.>