Recursive Parameter Estimation of Fractional Order Hammerstein Output Error Autoregressive Model.
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| Title: | Recursive Parameter Estimation of Fractional Order Hammerstein Output Error Autoregressive Model. |
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| Authors: | Li, Yanan1 (AUTHOR) 1938264040@qq.com, Li, Junhong1 (AUTHOR) missjunhong@163.com, Li, Fuchao1 (AUTHOR) 2212320017@stmail.ntu.edu.cn, Duan, Yaqi1 (AUTHOR) 221231006@stmail.ntu.edu.cn |
| Source: | Circuits, Systems & Signal Processing. Jul2025, Vol. 44 Issue 7, p4828-4846. 19p. |
| Subjects: | Polynomial operators, Parameter estimation, Separation (Technology), Algorithms |
| Abstract: | This paper studies the parameter identification problem of the fractional order Hammerstein output error autoregressive (FO-H-OEAR) model. First, the fractional order is introduced into the polynomial operator of the Hammerstein model. Then the key term separation technology is used in the model derivation process to obtain the FO-H-OEAR model. Second, this paper integrates the recursive technique into the maximum likelihood principle and proposes the fractional order maximum likelihood recursive least squares (ML-RLS) algorithm. Then the forgetting factor stochastic gradient (F-SG) algorithm is proposed as a comparative algorithm. Finally, two simulation examples are given. The results of the two simulation experiments show that the ML-RLS algorithm has higher accuracy and smaller identification error compared with the F-SG algorithm. Therefore, the ML-RLS algorithm can effectively estimate the parameters of the FO-H-OEAR model. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This paper studies the parameter identification problem of the fractional order Hammerstein output error autoregressive (FO-H-OEAR) model. First, the fractional order is introduced into the polynomial operator of the Hammerstein model. Then the key term separation technology is used in the model derivation process to obtain the FO-H-OEAR model. Second, this paper integrates the recursive technique into the maximum likelihood principle and proposes the fractional order maximum likelihood recursive least squares (ML-RLS) algorithm. Then the forgetting factor stochastic gradient (F-SG) algorithm is proposed as a comparative algorithm. Finally, two simulation examples are given. The results of the two simulation experiments show that the ML-RLS algorithm has higher accuracy and smaller identification error compared with the F-SG algorithm. Therefore, the ML-RLS algorithm can effectively estimate the parameters of the FO-H-OEAR model. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 0278081X |
| DOI: | 10.1007/s00034-025-03016-w |
