Maximum likelihood recursive least squares estimation for multivariate equation-error ARMA systems. (English) Zbl 1398.93323
Summary: This paper focuses on the parameter estimation problems of multivariate equation-error systems. A recursive generalized extended least squares algorithm is presented as a comparison. Based on the maximum likelihood principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification models, each of which has only a parameter vector, and a coupled subsystem maximum likelihood recursive least squares identification algorithm is developed for estimating the parameter vectors of these submodels. The simulation example shows that the proposed algorithm is effective and has high estimation accuracy.
MSC:
| 93E10 | Estimation and detection in stochastic control theory |
| 93E24 | Least squares and related methods for stochastic control systems |
| 93E12 | Identification in stochastic control theory |
Keywords:
maximum likelihood recursive least squares estimation; multivariate equation-error ARMA systems; least squares identificationReferences:
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