Comparing autoregressive moving average (ARMA) coefficients determination using artificial neural networks with other techniques

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dc.contributor.authorAibinu, A. M.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.contributor.authorShafie, Amir A.
dc.contributor.authorNajeeb, Athaur R.
dc.date.accessioned2019-08-14T15:00:01Z
dc.date.available2019-08-14T15:00:01Z
dc.date.issued2008-06-28
dc.description.abstractAutoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system.en_US
dc.identifier.citationAibinu, A. M., Salami, M. J., Shafie, A. A., & Najeeb, A. R. (2008). Comparing autoregressive moving average (ARMA) coefficients determination using artificial neural networks with other techniques. Proc. of World Academy of Science, Engineering and Technol.en_US
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/485
dc.language.isoenen_US
dc.publisherProc. of World Academy of Science, Engineering and Technolen_US
dc.subjectAutoregressive Moving Averageen_US
dc.subjectCoefficientsen_US
dc.subjectBack Propagationen_US
dc.subjectModel Parametersen_US
dc.subjectNeural Networken_US
dc.subjectWeighten_US
dc.titleComparing autoregressive moving average (ARMA) coefficients determination using artificial neural networks with other techniquesen_US
dc.typeArticleen_US
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