Filtering Identification.Part J:Filtering-Based Auxiliary Model Hierarchical Generalized Extended Parameter Identification for Multivariable Box-Jenkins Systems
Filtering Identification.Part J:Filtering-Based Auxiliary Model Hierarchical Generalized Extended Parameter Identification for Multivariable Box-Jenkins Systems
For multivariable output-error autoregressive moving average(M-OEARMA)models,which are also called multivariable Box-Jenkins models,this paper investigates and proposes filtered auxiliary model hierarchical generalized extended stochastic gradient identi-fication methods,filtered auxiliary model hierarchical multi-innovation generalized extended stochastic gradient identification methods,filtered auxiliary model hierarchical generalized extended recursive gradient identification methods,filtered auxiliary model hierarchical multi-innovation generalized extended recursive gradient identification methods,filtered aux-iliary model hierarchical generalized extended least squares identification methods,and fil-tered auxiliary model hierarchical multi-innovation generalized extended least squares identi-fication methods by using the filtering identification idea and the auxiliary identification idea from available input-output data.These filtered auxiliary model hierarchical generalized ex-tended identification methods can be extended to other linear and nonlinear multivariable sto-chastic systems with colored noises.
关键词
参数估计/递推辨识/辅助模型辨识/多新息辨识/递阶辨识/滤波辨识/最小二乘/多变量系统
Key words
parameter estimation/recursive identification/auxiliary model identification/multi-innovation identification/hierarchical identification/filtering identification/least squares/multivariable system
维普
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