Masked multiple state space model identification using FRD and evolutionary optimization
Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector conta...
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| Format: | Article |
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IEEE Computer Society
2024
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| _version_ | 1824452056172199936 |
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| author | Efe, Mehmet Önder Kürkçü, Burak Kasnakoğlu, Coşku Mohamed, Zaharuddin Liu, Zhijie |
| author_facet | Efe, Mehmet Önder Kürkçü, Burak Kasnakoğlu, Coşku Mohamed, Zaharuddin Liu, Zhijie |
| author_sort | Efe, Mehmet Önder |
| collection | ePrints |
| description | Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector containing (A, B, C, D) quadruple’s numerical content and a FRD-associated mask vector set that approximates the spectral information available in each FRD set? This article proposes a genetic algorithm based optimization approach to determine the real parameter vector (A, B, C, D) and the binary mask vector through a sequential optimization scheme. We study state space models for matching FRD from multiple systems. Results show that the proposed optimization approach solves the problem and compresses multiple dynamical models into a single masked one. |
| first_indexed | 2025-02-19T02:44:27Z |
| format | Article |
| id | utm.eprints-108871 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2025-02-19T02:44:27Z |
| publishDate | 2024 |
| publisher | IEEE Computer Society |
| record_format | dspace |
| spelling | utm.eprints-1088712025-01-08T08:35:25Z http://eprints.utm.my/108871/ Masked multiple state space model identification using FRD and evolutionary optimization Efe, Mehmet Önder Kürkçü, Burak Kasnakoğlu, Coşku Mohamed, Zaharuddin Liu, Zhijie TK Electrical engineering. Electronics Nuclear engineering TK5101-6720 Telecommunication Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector containing (A, B, C, D) quadruple’s numerical content and a FRD-associated mask vector set that approximates the spectral information available in each FRD set? This article proposes a genetic algorithm based optimization approach to determine the real parameter vector (A, B, C, D) and the binary mask vector through a sequential optimization scheme. We study state space models for matching FRD from multiple systems. Results show that the proposed optimization approach solves the problem and compresses multiple dynamical models into a single masked one. IEEE Computer Society 2024-04-26 Article PeerReviewed Efe, Mehmet Önder and Kürkçü, Burak and Kasnakoğlu, Coşku and Mohamed, Zaharuddin and Liu, Zhijie (2024) Masked multiple state space model identification using FRD and evolutionary optimization. IEEE Transactions on Industrial Informatics, 20 (7). pp. 9861-9869. ISSN 1551-3203 http://dx.doi.org/10.1109/TII.2024.3388605 DOI:10.1109/TII.2024.3388605 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering TK5101-6720 Telecommunication Efe, Mehmet Önder Kürkçü, Burak Kasnakoğlu, Coşku Mohamed, Zaharuddin Liu, Zhijie Masked multiple state space model identification using FRD and evolutionary optimization |
| title | Masked multiple state space model identification using FRD and evolutionary optimization |
| title_full | Masked multiple state space model identification using FRD and evolutionary optimization |
| title_fullStr | Masked multiple state space model identification using FRD and evolutionary optimization |
| title_full_unstemmed | Masked multiple state space model identification using FRD and evolutionary optimization |
| title_short | Masked multiple state space model identification using FRD and evolutionary optimization |
| title_sort | masked multiple state space model identification using frd and evolutionary optimization |
| topic | TK Electrical engineering. Electronics Nuclear engineering TK5101-6720 Telecommunication |
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