Paper Info
Title
Identification and control of dynamic systems using recurrent fuzzy neural networks
Abstract
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
Year
DOI
Venue
2000
10.1109/91.868943
IEEE T. Fuzzy Systems
Keywords
Field
DocType
fnn-fuzzy inference engine,lyapunov stability approach,temporal problems,identification,neurocontrollers,fuzzy inference,feedback connections,universal approximation,nonlinear dynamical systems,dynamic fuzzy rule,recurrent fuzzy neural network,convergence analysis,dynamic fuzzy rules,nonlinear dynamic system,direct adaptive control,indirect adaptive control,fuzzy logic,convergence,feedback,control problem,adaptive control,fuzzy control,recurrent neural nets,rigorous proofs,stability,learning rates,time series prediction,lyapunov methods,fuzzy neural nets,recurrent multilayered connectionist network,fuzzy neural network,fuzzy systems,engines,neurofeedback,indexing terms,nonlinear system,dynamic system,neural network,control systems,control
Control theory,Fuzzy logic,Lyapunov stability,Artificial intelligence,Inference engine,Adaptive control,Fuzzy control system,Control system,Artificial neural network,Machine learning,Connectionism,Mathematics
Journal
Volume
Issue
ISSN
8
4
1063-6706
Citations 
PageRank 
References 
267
11.78
10
Authors
2
Search Limit
100267
Name
Order
Citations
PageRank
Ching-Hung Lee159742.31
Ching-Cheng Teng247231.50