Fuzzy Model Identification Using Support Vector Clustering Method

We have observed that the support vector clustering method proposed by Asa Ben Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik, (Journal of Machine Learning Research, (2001), 125–137) can provide cluster boundaries of arbitrary shape based on a Gaussian kernel abstaining from explicit calculations in the high-dimensional feature space. This allows us to apply the method to the training set for building a fuzzy model. In this paper, we suggested a novel method for fuzzy model identification. The premise parameters of rules of the model are identified by the support vector clustering method while the consequent ones are tuned by the least squares method. Our model does not employ any additional method for parameter optimization after the initial model parameters are generated. It gives also promising performances in terms of a large number of rules. We compared the effectiveness and efficiency of our model to the fuzzy neural networks generated by various input spacepartition techniques and some other networks.