A Fuzzy Neural Networks with Structure Learning

Abstract

This paper presents a novel clustering algorithm for the structure learning of fuzzy neural networks. Our novel clustering algorithm uses the reward and penalty mechanism for the adaptation of the fuzzy neural networks prototypes for every training sample. This new clustering algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure. No prior knowledge of the input data distribution is needed for initialization. All rules are self-created, and they automatically grow with more incoming data. Our learning algorithm shows that supervised clustering algorithms can be used for the structure learning for the on-line self-organizing fuzzy neural networks. The control of the inverted pendulum is finally used to demonstrate the effectiveness of our learning algorithm.