Skip to Main Content
In this paper, we present a new support vector fuzzy inference system (SVFIS) for nonlinear system modeling. The proposed SVFIS is constructed using the support vector machine which does not have a bias term. The number of fuzzy rules is reduced by adjusting the parameter values of membership functions using the gradient descent method. Once a structure is selected, the parameter values in the consequent part of the Tagaki-Sugeno (TS) fuzzy model are determined by the least square method. The simulation result illustrates the effectiveness of the proposed SVFIS.
Decision and Control, 2002, Proceedings of the 41st IEEE Conference on (Volume:2 )
Date of Conference: 10-13 Dec. 2002