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Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems

Feiyan Chen  Kai Ji  
【摘要】:Aiming at the problem of redundant parameters in the over-parameterized model,a maximum likelihood algorithm for a class of multi-input nonlinear systems is studied based on the data filtering technique.This paper develops a filtering based maximum likelihood generalized extended gradient algorithm for multivariable nonlinear systems with autoregressive moving average noises and a maximum likelihood generalized extended gradient algorithm for comparison.Finally,the simulation results indicate that the derived algorithms are more effective for identifying multivariable nonlinear systems.

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