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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12656

Title: Cumulants and genetic algorithm for parameters estimation of noncausal autoregressive models
Authors: Alshebeili, S.A.
Alsehaili, M.A.
Alkanhal, M.A.
Keywords: Fourier transforms; Genetic algorithms; Least squares approximations; Linear equations; Mathematical models; Matrix algebra; Parameter estimation; Random processes; Signal to noise ratio; Z transforms
Issue Date: 2002
Citation: International Journal of Modelling and Simulation Volume 22, Issue 3, 2002, Pages 186-196
Abstract: The authors introduce a new method for estimating the coefficients of a noncausal autoregressive (AR) model. This method is based on a new formulation that relates the unknown AR parameters to both second- and third-order cumulants. The new formulation facilitates the use of linear and nonlinear least-square estimation techniques, and includes some published works as a special case. The nonlinear least-square estimation techniques presented in this work make use of a genetic algorithm (GA) to minimize a cost function that is defined in terms of the model's output cumulants. We also introduce a new method for estimating the coefficients of a noncausal AR model using the power spectrum and a one-dimensional (1-D) slice of the bispectrum. To illustrate the effectiveness of the proposed AR modelling approaches, extensive simulation examples are presented.
URI: http://hdl.handle.net/123456789/12656
ISSN: 02286203
Appears in Collections:College of Engineering

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