An Extended Sliding Mode Learning Algorithm for Type-2 Fuzzy Neural Networks

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for type-2 fuzzy neural networks is proposed in this paper. It can be considered as an extended version of the recently developed on-line learning approaches for type-2 fuzzy neural networks based on the Variable Structure System theory concepts. Simulation results from the identification of a nonlinear system with uncertainties have demonstrated the better performance of the proposed extended algorithm in comparison with the previously reported in the literature sliding mode learning algorithms for both type-1 and type-2 fuzzy neural structures.