Filtering Identification.Part L:Filtering-Based Auxiliary Model Hierarchical Generalized Extended Iterative Parameter Identification for Multivariable OEARMA Systems
For multivariable output-error autoregressive moving average(M-OEARMA)models,which are also called multivariable Box-Jenkins(M-BJ)models,this paper investi-gates and proposes filtered auxiliary-model hierarchical generalized extended gradient-based iterative identification methods,filtered auxiliary-model hierarchical multi-innovation gener-alized extended gradient-based iterative identification methods,filtered auxiliary-model hier-archical generalized extended least squares-based iterative identification methods,and filtered auxiliary-model hierarchical multi-innovation generalized extended least squares-based itera-tive identification methods by using the filtering identification idea and the auxiliary-model i-dentification idea from available input-output data.These filtered auxiliary-model hierarchi-cal generalized extended iterative identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noises.
parameter estimationiterative identificationmulti-innovation identificationhi-erarchical identificationfiltering identificationleast squaresmultivariable system
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