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Decomposition-based maximum likelihood gradient iterative algorithm for multivariate systems with colored noise
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DOI：10.1002/rnc.7344.png)
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Abstract
En 中文
In this paper, we use the maximum likelihood principle and the negative gradient search principle to study the identification issues of the multivariate equation-error systems whose outputs are contaminated by an moving average noise process. The model decomposition technique is used to decompose the system into several regressive identification subsystems based on the number of the outputs. In order to improve the parameter estimation accuracy, a decomposition-based multivariate maximum likelihood gradient iterative algorithm is proposed by means of the maximum likelihood principle and the iterative identification method. The numerical simulation example indicates that the proposed method has better parameter estimation results than the compared decomposition-based multivariate maximum likelihood gradient algorithm.
Keywords:
iterative identification
maximum likelihood
model decomposition
multivariate system
negative gradient search
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