Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea
For equation‐error autoregressive moving average systems, that is, Box–Jenkins systems, this paper presents a filtered auxiliary model generalized extended stochastic gradient identification method, a filtered auxiliary model multi‐innovation generalized extended stochastic gradient identification m...
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| Published in | International journal of robust and nonlinear control Vol. 33; no. 10; pp. 5510 - 5535 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Wiley Subscription Services, Inc
10.07.2023
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| Abstract | For equation‐error autoregressive moving average systems, that is, Box–Jenkins systems, this paper presents a filtered auxiliary model generalized extended stochastic gradient identification method, a filtered auxiliary model multi‐innovation generalized extended stochastic gradient identification method, a filtered auxiliary model recursive generalized extended gradient identification method, a filtered auxiliary model multi‐innovation recursive generalized extended gradient identification method, a filtered auxiliary model recursive generalized extended least squares identification method, and a filtered auxiliary model multi‐innovation recursive generalized extended least squares identification method by using the filtering identification idea and the auxiliary model identification idea. The proposed filtered auxiliary model recursive generalized extended identification methods can be generalized to other linear and nonlinear multivariable stochastic systems with colored noises. |
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| AbstractList | For equation‐error autoregressive moving average systems, that is, Box–Jenkins systems, this paper presents a filtered auxiliary model generalized extended stochastic gradient identification method, a filtered auxiliary model multi‐innovation generalized extended stochastic gradient identification method, a filtered auxiliary model recursive generalized extended gradient identification method, a filtered auxiliary model multi‐innovation recursive generalized extended gradient identification method, a filtered auxiliary model recursive generalized extended least squares identification method, and a filtered auxiliary model multi‐innovation recursive generalized extended least squares identification method by using the filtering identification idea and the auxiliary model identification idea. The proposed filtered auxiliary model recursive generalized extended identification methods can be generalized to other linear and nonlinear multivariable stochastic systems with colored noises. |
| Author | Zhang, Xiao Ding, Feng Xu, Ling Zhou, Yihong |
| Author_xml | – sequence: 1 givenname: Feng orcidid: 0000-0002-2721-2025 surname: Ding fullname: Ding, Feng email: fding@jiangnan.edu.cn organization: Qingdao University of Science and Technology – sequence: 2 givenname: Ling orcidid: 0000-0002-5040-5634 surname: Xu fullname: Xu, Ling organization: Jiangnan University – sequence: 3 givenname: Xiao orcidid: 0000-0002-6413-6148 surname: Zhang fullname: Zhang, Xiao organization: Jiangnan University – sequence: 4 givenname: Yihong orcidid: 0000-0002-6483-9927 surname: Zhou fullname: Zhou, Yihong organization: Jiangnan University |
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| Snippet | For equation‐error autoregressive moving average systems, that is, Box–Jenkins systems, this paper presents a filtered auxiliary model generalized extended... |
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| SubjectTerms | Autoregressive moving average auxiliary model identification filtering identification Filtration gradient search Identification methods Innovations Least squares multi‐innovation identification Parameter estimation Stochastic systems |
| Title | Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea |
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