Building a Fuzzy System from Input–Output Data

Abstract

We propose a new neural network for implementing fuzzy systems, and we prove that it can represent any continuous function over a compact set. We propose and test a method for building a fuzzy neural system from input-output data. We analyze the output data using fuzzy c-means to obtain the number of rules and to set some of the initial weights in the network. Then, we use this fuzzy neural network to identify the input variables and to determine the number of input membership functions. We show that the resulting model is simpler and yields better performance than previously proposed methods for extracting fuzzy systems and neural networks from input-output data.