TITLE:
Neural Modeling of Multivariable Nonlinear Stochastic System. Variable Learning Rate Case
AUTHORS:
Ayachi Errachdi, Ihsen Saad, Mohamed Benrejeb
KEYWORDS:
Neural Networks, Multivariable System, Stochastic, Learning Rate, Modeling
JOURNAL NAME:
Intelligent Control and Automation,
Vol.2 No.3,
August
8,
2011
ABSTRACT: 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.