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changes in examples.

6. Conclusions

In this paper, a variable learning rate for neural modeling

of multivariable nonlinear stochastic system is suggested.

This parameter can slow down the training phase when it

is chosen as small, and can be unstable when it is chosen

as large. To avoid this step, a variable learning rate

method is developed and it is applied in identification of

nonlinear stochastic system. The advantages of the pro-

posed algorithm are firstly the simplicity to apply it in a

multi-input multi-output nonlinear system. Secondly, the

gain of the training time is remarked and the result qual-

ity is noticed. Besides, this algorithm is a manner to

avoid the search for such fixed training rate which pre-

sents a disadvantage at the level the phase of training. In

contrary, the variable learning rate algorithm does not

require any experimentation for the selection of an ap-

propriate value of the learning rate. The proposed algo-

rithm can be applied in real time process modeling. Dif-

ferent cases of SNR are discussed to test the developed

method and it showed that the obtained results using a

variable learning rate is very satisfy than when the fixed

learning rate was used.

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