Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter
Summary This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hi...
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| Veröffentlicht in: | International journal of adaptive control and signal processing Jg. 37; H. 7; S. 1827 - 1846 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Bognor Regis
Wiley Subscription Services, Inc
01.07.2023
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| ISSN: | 0890-6327, 1099-1115 |
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| Abstract | Summary
This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect. |
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| AbstractList | Summary
This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect. This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect. |
| Author | Ji, Yan Bi, Yiqun |
| Author_xml | – sequence: 1 givenname: Yiqun surname: Bi fullname: Bi, Yiqun organization: Qingdao University of Science and Technology – sequence: 2 givenname: Yan orcidid: 0000-0002-3245-2437 surname: Ji fullname: Ji, Yan email: yjichina@163.com organization: Qingdao University of Science and Technology |
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This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored... This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An... |
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| SubjectTerms | Algorithms Extended Kalman filter extended Kalman filtering forgetting factor fractional‐order system hierarchical identification Parameter estimation Parameter identification State estimation stochastic gradient algorithm |
| Title | Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter |
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