Identification and control of dynamic systems using recurrent fuzzy neural networks

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 addin...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2000-08, Vol.8 (4), p.349-366
Main Authors: Lee, Ching-Hung, Teng, Ching-Cheng
Format: Article
Language:English
Subjects:
Adaptive control
Computer simulation
Control systems
Convergence
Dynamical systems
Engines
Fuzzy
Fuzzy control
Fuzzy logic
Fuzzy neural networks
Fuzzy set theory
Fuzzy systems
Lyapunov method
Neural networks
Neurofeedback
Nonlinear control systems
Nonlinear dynamics
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Summary: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.
ISSN:1063-6706
1941-0034
DOI:10.1109/91.868943