Abstract
This paper deals with the identification of nonlinear complex systems using the Nonlinear Auto Regressive Moving Average (NARMA) model. We propose, jor the monovariable systems, two new approaches allowing the determination of the significant terms in the expression of NARMA model as well as the estimate of the corresponding coefficients. The first one exploits the Binary Genetic Algorithm, whereas the second uses a procedure based on a single layer neural network with polynomial activation function which is optimised with a real-coded genetic algorithm. We propose, also, the extension of the second method to identify the multivariable nonlinear systems.
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