There are some problems in fuzzy system for modeling and identification, such as complexity of model construction, curse of dimensionality, poverty of generalization and error of real-time. To deal with these problems, support vector mechanism (SVM) for fuzzy system modeling has been introduced in this paper. And then the parameters have been optimized by error back-propagation training algorithm (BP algorithm). Experimental results demonstrate the effectiveness of the method.
['yanli Deng', 'Jun Wang', 'Xiaodan Yan']
T-S fuzzy system identification based on support vector machine