General type-2 fuzzy neural network with hybrid learning for function approximation
Wen-Hau Roger Jeng, Chi-Yuan Yeh, Shie-Jue Lee · 2009
A novel Takagi-Sugeno-Kang (TSK) type fuzzy neural network which uses general type-2 fuzzy sets in a type-2 fuzzy logic system, called general type-2 fuzzy neural network (GT2FNN), is proposed for function approximation. The problems of constructing a GT2FNN include type reduction, structure identification, and parameter identification. An efficient strategy is proposed by using alpha-cuts to decompose a general type-2 fuzzy set into several interval type-2 fuzzy sets to solve the type reduction problem. Incremental similarity-based fuzzy clustering and linear least squares regression are combined to solve the structure identification problem. Regarding the parameter identification, a hybrid learning algorithm (HLA) which combines particle swarm optimization (PSO) and recursive least squares (RLS) estimator is proposed for refining the antecedent and consequent parameters, respectively, of fuzzy rules. Simulation results show that the resulting networks obtained are robust against outliers.