DocumentCode :
3469353
Title :
On-line identification of multivariable nonlinear system using neural networks
Author :
Errachdi, Ayachi ; Saad, Ismail ; Benrejeb, Mohamed
Author_Institution :
U.R. LARA Autom., Univ. of Sousse, Tunis, Tunisia
fYear :
2011
fDate :
3-5 March 2011
Firstpage :
1
Lastpage :
5
Abstract :
In this paper, an on-line identification method based on recurrent neural networks (RNN) proposed for multivariable nonlinear systems. This work is an extension of an on-line method for single-input single output system. The large number of input-output vectors is being considered. As the complexity and nonlinearity of the systems is treated. The effectiveness of the proposed algorithm applied to two examples of multivariable nonlinear dynamic systems is demonstrated by simulation experiments. The results of simulation showed that the use of the neural networks is helpful for adaptive strategy design.
Keywords :
adaptive control; identification; multivariable control systems; nonlinear dynamical systems; recurrent neural nets; adaptive strategy design; multivariable nonlinear dynamic systems; online identification method; recurrent neural networks; single-input single output system; systems complexity; Nickel; Nonlinear system; modeling; multivariable; neural networks; on-line identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, Computing and Control Applications (CCCA), 2011 International Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4244-9795-9
Type :
conf
DOI :
10.1109/CCCA.2011.6031501
Filename :
6031501
Link To Document :
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