Abstract
Abstract This paper proposes a Self-Evolving Interval Type-2 Fuzzy Neural Network (SEIT2FNN) for nonlinear systems identification. The SEIT2FNN has both on-line structure and parameter learning abilities. The antecedent parts in each fuzzy rule of the SEIT2FNN are interval type-2 fuzzy sets and the fuzzy rules are of the Takagi-Sugeno-Kang (TSK) type. An on-line clustering method is proposed to generate fuzzy rules which flexibly partition the input space. For parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm for high accuracy learning performance. The antecedent part parameters are learned by gradient descent algorithms. Comparisons with identification using other type-1 and type-2 fuzzy systems verify the performance of the SEIT2FNN.
Submitted Version (
Free)
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call