Fuzzy rule-based systems are nowadays one of the most successful applications of fuzzy logic, but in complex applications with a large set of variables, the number of rules increases exponentially and the obtained fuzzy system is scarcely interpretable. Hierarchical fuzzy systems are one of the alternatives presented in the literature to overcome this problem. This paper presents a multilevel fuzzy support vector regression network (MFSVRN) model that learns incremental hierarchical structure based on the Takagi-Sugeno-Kang(TSK) fuzzy system with the aim of coping with the curse of dimensionality and generalization ability. From the input–output data pairs, the TS-type rules and its parameters are learned by a combination of fuzzy clustering and linear SVR in this paper. In addition, an efficient input variable selection method of the incremental multilevel network is proposed based on the FCM clustering and fuzzy association rules. To achieve high generalization ability, the consequence parameters of a rule are learned through linear SVR with a new TS-kernel. This paper demonstrates the capabilities of MFSVRN model by conducting simulations in function approximations and a chaotic time-series prediction. This paper also compares simulation results from the single-level counterparts- FSVRN and Jang's ANFIS model.
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