Fuzzy inference neural network

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.