DocumentCode :
1416421
Title :
Black-Box Identification of a Class of Nonlinear Systems by a Recurrent Neurofuzzy Network
Author :
González-Olvera, Marcos A. ; Tang, Yu
Author_Institution :
Coll. of Sci. & Technol., Autonomous Univ. of Mexico City, Mexico City, Mexico
Volume :
21
Issue :
4
fYear :
2010
fDate :
4/1/2010 12:00:00 AM
Firstpage :
672
Lastpage :
679
Abstract :
This brief presents a structure for black-box identification based on continuous-time recurrent neurofuzzy networks for a class of dynamic nonlinear systems. The proposed network catches the dynamics of a system by generating its own states, using only input and output measurements of the system. The training algorithm is based on adaptive observer theory, the stability of the network, the convergence of the training algorithm, and the ultimate bound on the identification error as well as the parameter error are established. Experimental results are included to illustrate the effectiveness of the proposed method.
Keywords :
continuous time systems; fuzzy neural nets; identification; neurocontrollers; nonlinear control systems; recurrent neural nets; stability; adaptive observer theory; black-box identification; continuous-time recurrent neurofuzzy network; convergence; dynamic nonlinear system; identification error; parameter error; stability; system identification; training algorithm; Nonlinear systems; recurrent neural network; system identification; Algorithms; Computer Simulation; Feedback; Fuzzy Logic; Humans; Linear Models; Neural Networks (Computer); Nonlinear Dynamics; Reproducibility of Results;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2010.2041068
Filename :
5411934
Link To Document :
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