標題: Identification and control of dynamic systems using recurrent fuzzy neural networks
作者: Lee, CH
Teng, CC
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: control;fuzzy logic;fuzzy neural network (FNN);identification;neural network
公開日期: 1-Aug-2000
摘要: 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. fn 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.
URI: http://dx.doi.org/10.1109/91.868943
http://hdl.handle.net/11536/30338
ISSN: 1063-6706
DOI: 10.1109/91.868943
期刊: IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume: 8
Issue: 4
起始頁: 349
結束頁: 366
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