Linear combination rule in genetic algorithm for optimization of finite impulse response neural network to predict natural chaotic time series

A finite impulse response neural network, with tap delay lines after each neuron in hidden layer, is used. Genetic algorithm with arithmetic decimal crossover and Roulette selection with normal probability mutation method with linear combination rule is used for optimization of FIR neural network. T...

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Bibliographic Details
Published in:Chaos, solitons and fractals solitons and fractals, 2009-09, Vol.41 (5), p.2681-2689
Main Author: Mirzaee, Hossein
Format: Article
Language:English
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Summary:A finite impulse response neural network, with tap delay lines after each neuron in hidden layer, is used. Genetic algorithm with arithmetic decimal crossover and Roulette selection with normal probability mutation method with linear combination rule is used for optimization of FIR neural network. The method is applied for prediction of several important and benchmarks chaotic time series such as: geomagnetic activity index natural time series and famous Mackey–Glass time series. The results of simulations shows that applying dynamic neural models for modeling of highly nonlinear chaotic systems is more satisfactory with respect to feed forward neural networks. Likewise, global optimization method such as genetic algorithm is more efficient in comparison of nonlinear gradient based optimization methods like momentum term, conjugate gradient.
ISSN:0960-0779
1873-2887
DOI:10.1016/j.chaos.2008.09.057