Abstract: GK fuzzy clustering is a data analysis technique that is widely applied to classification, and it can intelligently detect the shape of a different cluster. However, there are shortcomings about constant numbers of clusters in each iteration and the covariance matrix in the formula required to nonzero. In order to improve these shortcomings, this paper proposes a new fuzzy clustering algorithm. The existing fuzzy identification algorithm has the curse of dimensionality problem and it lacks of a solid theoretical foundation. The paper analyzes the equivalence of the lack of bias item support vector regression machine and languagebased fuzzy model, proposes a new algorithm which combines support vector machines and fuzzy model identification. It also uses gradient descent method to identify parameters. In order to reduce the number of rules and reflect the data information, the paper adopts an improved GK fuzzy clustering for the input sample set to classify these data better.

Key words: support vector machines, fuzzy cluster, fuzzy system identification