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Neural Modeling of Multivariable Nonlinear Stochastic System. Variable Learning Rate Case

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DOI: 10.4236/ica.2011.23020    3,847 Downloads   6,506 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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The authors declare no conflicts of interest.

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A. Errachdi, I. Saad and M. Benrejeb, "Neural Modeling of Multivariable Nonlinear Stochastic System. Variable Learning Rate Case," Intelligent Control and Automation, Vol. 2 No. 3, 2011, pp. 167-175. doi: 10.4236/ica.2011.23020.


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