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Maximum likelihood gradient identification for multivariate equation-error moving average systems using the multi-innovation theory
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DOI：10.1002/acs.3007.png)
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Abstract
En 中文
For the multivariate equation-error moving average system, a multivariate maximum likelihood multi-innovation extended stochastic gradient (M-ML-MIESG) algorithm is delivered. The key is to decompose the system into several regressive identification subsystems according to the number of the system outputs. Then, a multivariate maximum likelihood extended stochastic gradient algorithm is presented to estimate the parameters of these subsystems. The M-ML-MIESG algorithm has higher parameter estimation accuracy than the multivariate extended stochastic gradient algorithm. The simulation examples indicate that the proposed methods work well.
Keywords:
gradient identification
maximum likelihood
multi-innovation
multivariate system
parameter estimation
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