A Block-Diagonal Recurrent Fuzzy Neural Network for Dynamic System Identification

Abstract

A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled Dynamic Block-Diagonal Fuzzy Neural Network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark problem, where a dynamic system is to be identified. A comparative analysis with a series of recurrent fuzzy and neural models is conducted, highlighting the modeling characteristics of DBD-FNN.