Comparing Autoregressive Moving Average (ARMA) coefficients determination using artificial neural network with other techniques
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 a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Waset
2008
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/57690/ http://irep.iium.edu.my/57690/ http://irep.iium.edu.my/57690/1/57690_Comparing%20Autoregressive%20Moving%20Average.pdf |
| Summary: | 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 alsogives accurate in computing the coefficients of an ARMA system. |
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