Neural Modeling of Multivariable Nonlinear Stochastic System. Variable Learning Rate Case
Ayachi Errachdi, Ihsen Saad, Mohamed Benrejeb
DOI: 10.4236/ica.2011.23020   PDF    HTML     4,435 Downloads   7,395 Views   Citations


The objective of this paper is to develop a variable learning rate for neural modeling of multivariable nonlinear stochastic system. The corresponding parameter is obtained by gradient descent method optimization. The effectiveness of the suggested algorithm applied to the identification of behavior of two nonlinear stochastic systems is demonstrated by simulation experiments.

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Errachdi, A. , Saad, I. and Benrejeb, M. (2011) Neural Modeling of Multivariable Nonlinear Stochastic System. Variable Learning Rate Case. Intelligent Control and Automation, 2, 167-175. doi: 10.4236/ica.2011.23020.

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The authors declare no conflicts of interest.


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