Identification and control of dynamic systems using recurrent fuzzy neural networks

Ching Hung Lee, Ching Cheng Teng

Research output: Contribution to journalArticle

539 Scopus citations

Abstract

This paper proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN). The RFNN expands the basic ability of the FNN to cope with temporal problems. In addition, results for the FNN-fuzzy inference engine, universal approximation, and convergence analysis are extended to the RFNN. For the control problem, we present the direct and indirect adaptive control approaches using the RFNN. Based on the Lyapunov stability approach, rigorous proofs are presented to guarantee the convergence of the RFNN by choosing appropriate learning rates. Finally, the RFNN is applied in several simulations (time series prediction, identification, and control of nonlinear systems). The results confirm the effectiveness of the RFNN.

Original languageEnglish
Pages (from-to)349-366
Number of pages18
JournalIEEE Transactions on Fuzzy Systems
Volume8
Issue number4
DOIs
StatePublished - Aug 2000

Fingerprint Dive into the research topics of 'Identification and control of dynamic systems using recurrent fuzzy neural networks'. Together they form a unique fingerprint.

  • Cite this