Incremental Hierarchical Fuzzy Model Generated from Multilevel Fuzzy Support Vector Regression Network

Abstract

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.