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The residual-based ESG algorithm and its performance analysis
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DOI：10.1016/j.jfranklin.2009.05.008.png)
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The performance of the residual-based extended stochastic gradient (ESG) algorithms for identifying CARMA models with disturbances is analyzed under weaker conditions on statistical properties of the noise. The paper derives the conditions under which the parameter estimation errors converge to zero. Three examples are given to show the advantages of the proposed algorithm. (C) 2009 Published by Elsevier Ltd. on behalf of The Franklin Institute.
Keyword:
Recursive identification
Parameter estimation
Convergence properties
Stochastic gradient
Stochastic approximation
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