Filtering Identification.Part K:Filtering-Based Hierarchical Generalized Extended Iterative Parameter Identification for Multivariable Controlled Autoregressive Autoregressive Moving Average Systems
For multivariable controlled autoregressive autoregressive moving average(M-CARARMA)models,which are also called multivariable equation-error autoregressive mov-ing average(M-EEARMA)models,this paper investigates and proposes filtered hierarchical generalized extended gradient-based iterative identification methods,filtered hierarchical multi-innovation generalized extended gradient-based iterative identification methods,fil-tered hierarchical generalized extended least squares-based iterative identification methods,and filtered hierarchical multi-innovation generalized extended least squares-based iterative i-dentification methods from available input-output data by using the filtering identification i-dea and the hierarchical identification principle.These filtered hierarchical generalized ex-tended iterative identification methods can be extended to other linear and nonlinear multiva-riable stochastic systems with colored noises.
parameter estimationiterative identificationmulti-innovation identificationhi-erarchical identificationfiltering identificationleast squaresmultivariable system
万方数据
维普
