Hierarchical multi‐innovation generalised extended stochastic gradient methods for multivariable equation‐error autoregressive moving average systems
Copyright policy )Hierarchical multi‐innovation generalised extended stochastic gradient methods for multivariable equation‐error autoregressive moving average systems
This study presents the modelling technology of multivariable equation‐error autoregressive moving average (EEARMA) systems through observational data of systems. Aiming to develop a simplified identification algorithm, the original multivariable EEARMA model to be identified is separated into two sub‐identification models. After the model decomposition, a two‐stage generalised extended stochastic gradient (GESG) algorithm is presented in accordance with these two separated submodels. By adding more observations to the recursive computation, the corresponding two‐stage multi‐innovation GESG (MI‐GESG) algorithm, namely, hierarchical multi‐innovation generalised extended stochastic gradient algorithm, is derived for the multivariable EEARMA systems through expanding the innovation vector to the innovation matrices. The simulation example verifies that the performance about the computational accuracy of the two‐stage MI‐GESG algorithm is improved compared with the two‐stage GESG algorithm.
- Ministry of Education of the People's Republic of China China (People's Republic of)
- University of Mary United States
- Qingdao University of Science and Technology China (People's Republic of)
- University of Washington United States
- Jiangnan University China (People's Republic of)
multivariable equation-error autoregressive moving average systems, model decomposition, two-stage multiple innovation GESG algorithm, Multivariable systems, multidimensional control systems, innovation matrices, 510, Hierarchical systems, two-stage multiinnovation gradient methods, two-stage GESG algorithm, sub-identification models, identification, multivariable EEARMA systems, stochastic processes, System identification, multivariable EEARMA model, two-stage MI-GESG algorithm, gradient methods, autoregressive moving average processes, two-stage generalised extended stochastic gradient algorithm
multivariable equation-error autoregressive moving average systems, model decomposition, two-stage multiple innovation GESG algorithm, Multivariable systems, multidimensional control systems, innovation matrices, 510, Hierarchical systems, two-stage multiinnovation gradient methods, two-stage GESG algorithm, sub-identification models, identification, multivariable EEARMA systems, stochastic processes, System identification, multivariable EEARMA model, two-stage MI-GESG algorithm, gradient methods, autoregressive moving average processes, two-stage generalised extended stochastic gradient algorithm
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