A Self-organizing Fuzzy Neural Networks

Abstract

This paper proposes a novel clustering algorithm for the structure learning of fuzzy neural networks. Our clustering algorithm uses the reward and penalty mechanism for the adaptation of the fuzzy neural networks prototypes at every training sample. Compared with the classical clustering algorithms, the new algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure. No priori knowledge of the input data distribution is needed for initialization. All rules are self-created, and they grow automatically with more incoming data. There are no conflicting rules in the created fuzzy neural networks. Our approach also shows that supervised clustering algorithms can be used for the structure learning of the self-organizing fuzzy neural networks. The identification of several typical nonlinear dynamic systems is developed to demonstrate the effectiveness of this learning algorithm.