A Least-Squares-Based Algorithm for Identification of Non-Gaussian ARMA Models |
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Authors: | Adnan Al-Smadi |
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Affiliation: | (1) Department of Computer Science, Prince-Hussein Bin Abdullah College for Information Technology, Al Al-Bayt University, Al-Mafraq, Jordan, on sabbatical leave from the College of Engineering, Yarmouk University, Jordan |
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Abstract: | A new recursive method for estimating the parameters of autoregressive moving average (ARMA) models is presented in this paper.
The recursive linear identification method is developed using higher-order statistics of the observed output data and is
based on a least-squares solution. Namely, a matrix consisting of third-order statistics (or cumulants) of the observed output
data is constructed so that it almost possesses a full rank structure. The signal is embedded in a Gaussian noise that may
be colored. The system is driven by a zero-mean independent and identically distributed non-Gaussian process. The excitation
signal is unobserved. Simulation results are given to illustrate the performance of the proposed algorithm with respect to
existing well-known methods. |
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Keywords: | |
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