Comparing autoregressive moving average (ARMA) coefficients determination using artificial neural networks with other techniques
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Date
2008-06-28
Journal Title
Journal ISSN
Volume Title
Publisher
Proc. of World Academy of Science, Engineering and Technol
Abstract
Autoregressive 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.
Description
Keywords
Autoregressive Moving Average, Coefficients, Back Propagation, Model Parameters, Neural Network, Weight
Citation
Aibinu, 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.