Identification Based on TS-GFNN (Takagi-Sugeno Generalized Fuzzy Neural Network)
Ruihua Wei, Lihong Xu · 2006
GFNN (generalized fuzzy neural network), which integrates the advantages of neural network into that of the fuzzy logic system, is a powerful method in the modeling of the nonlinear system. However, it is difficult for the GFNN to be used as a model in the traditional way of designing the controller, because GFNN is intrinsically nonlinear. A new design of GFNN based on T-S (Takagi-Sugeno) model and its corresponding off-line architecture and parameter identification algorithm is presented in the paper. In addition, to better use the on-line self-adjusting advantages of GFNN, the on-line architecture-self-organizing and parameter-self-learning algorithm is also presented. The on-line identification algorithm can make the TS-GFNN to be more adaptive in the design of controller.