An Internal/Interconnection Recurrent Type-2 Fuzzy Neural Network (IRT2FNN) for Dynamic System Identification

Abstract

This paper proposes an Internal/Interconnection Recurrent Type-2 Fuzzy Neural Network (IRT2FNN) for dynamic system identification. The antecedent part of IRT2FNN forms a self and interconnection feedback loop by feeding the past and current firing strength of each rule. The TSK-type consequent part is a linear model of exogenous inputs with interval weights. The initial rule base in the IRT2FNN is empty, and an on-line constructing method is proposed to generate fuzzy rules which flexibly partition the input space. The recurrent structure in the IRT2FNN enable to handle dynamic system identification problems with a priori knowledge of system input and output delay numbers. Simulations on dynamic system identification verify the performance of IRT2FNN with clean and noisy outputs as well.