Incremental Hierarchical Fuzzy Model Generated from Multilevel Fuzzy Support Vector Regression Network (Informatica, 2014)
Fuzzy rule-based systems are nowadays one of the most successful applications of fuzzy logic, but incomplex applications with a large set of variables, the number of rules increases exponentially and theobtained fuzzy system is scarcely interpretable. Hierarchical fuzzy systems are one of the alternativespresented in the literature to overcome this problem. This paper presents a multilevel fuzzy supportvector regression network (MFSVRN) model that learns incremental hierarchical structure based on theTakagi-Sugeno-Kang(TSK) fuzzy system with the aim of coping with the curse of dimensionality andgeneralization ability. From the input–output data pairs, the TS-type rules and its parameters arelearned by a combination of fuzzy clustering and linear SVR in this paper. In addition, an efficient inputvariable selection method of the incremental multilevel network is proposed based on the FCMclustering and fuzzy association rules. To achieve high generalization ability, the consequenceparameters of a rule are learned through linear SVR with a new TS-kernel. This paper demonstrates thecapabilities of MFSVRN model by conducting simulations in function approximations and a chaotictime-series prediction. This paper also compares simulation results from the single-level counterparts-FSVRN and Jang's ANFIS model.