Maximum Likelihood Recursive Parameter Estimation for Bilinear Parametric Systems
In this paper we study bilinear parametric systems with autoregressive moving av-erage noise.The structure of the system is complex,and the study of the noise term is of more general significance.In order to realize the on-line identification of system parameters,the stochastic gradient algorithm of bilinear parameter system is derived by using gradient search method.For unknown terms,the estimated value is used to replace them based on the principle of hierarchical identification.Maximum likelihood estimation method based on probability theory,has a good consistency,asymptotic normality and availability,after the introduction of maximum likelihood estimation method to get the corresponding maximum likelihood stochastic gradient algorithm,in order to further reduce the influence of colored noise on parameters estimation precision,combining many new interest identification theo-ry,the extension of the scalar single new rates for new interest vector,A maximum likeli-hood multi-information stochastic gradient parameter estimation method for bilinear para-metric systems with autoregressive moving average noise is studied and verified by simula-tion.
system identificationbilinear parameter systemsgradient searchmaximum like-lihood
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