A Evolving Fuzzy Classifier System Based on Ellipsoidal Regions
Yang Ai · 2005
This paper introduces an evolving fuzzy classifier system. At first, the basic characteristics and frame of this system are introduced. Then, the dynamic clustering arithmetic which can dynamically cluster the input training patterns is presented. For every cluster, a fuzzy rule with an ellipsoidal region around a cluster center is defined. The strategy of tuning fuzzy rules is that the slopes of the membership functions are tuned successively until there is no improvement in the recognition rate of the training patterns. If this tuning can not satisfy the request, Genetic Algorithms will be used. In this paper, the tuning method and arithmetic, the policy of inserting rules and aggregating rules are discussed. This method has been evaluated by two typical data sets. The recognition rates of our classifier are comparable to the maximum recognition rates of the multilayered neural network classifier, and its training time is much shorter.