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.
Highlights
The Neural Networks (NN) was well used in modeling of nonlinear systems because of its ability of learning, its generalization and its approximation [1,2,3,4]
A variable learning rate method is developed and it is applied in identification of nonlinear stochastic system
The advantages of the proposed algorithm are firstly the simplicity to apply it in a multi-input multi-output nonlinear system
Summary
The Neural Networks (NN) was well used in modeling of nonlinear systems because of its ability of learning, its generalization and its approximation [1,2,3,4] This approach provides an effective solution for wide classes of nonlinear systems which are not known or only partial state information is available [5]. The measured output system is tainted noise This is due either to the effect of disturbances acting at different parts of the process, either to measurement noise. These noises may introduce errors in the identification. A multivariable nonlinear stochastic system is our interest
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