A new approach to TS fuzzy modeling using dual kernel-based learning machines

This paper proposes a novel approach for structure identification of TS fuzzy model using dual kernel-based learning machines. Firstly, a convenient kernel fuzzy C-means clustering algorithm is developed to partition the data set into several clusters. Secondly, a new kernel function which is free of parameter selection is utilized to locate support vectors in each cluster. Finally, the model structure is further simplified by a combination strategy for support vectors. The experimental results show that the resulting model has concise structure and good generalization ability, especially its performance is insensitive to initial clustering number. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.