Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory
With the development of industrial demand, precise identification of system models is currently required in the field of industrial control, which limits the whale search algorithm. In response to the fact that whale optimization algorithms are prone to falling into local optima and the identificati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2024-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2024/5619098 |
| _version_ | 1797289893459656704 |
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| author | Lu Zhao Jiangjun Liu Yuan Li |
| author_facet | Lu Zhao Jiangjun Liu Yuan Li |
| author_sort | Lu Zhao |
| collection | DOAJ |
| description | With the development of industrial demand, precise identification of system models is currently required in the field of industrial control, which limits the whale search algorithm. In response to the fact that whale optimization algorithms are prone to falling into local optima and the identification of important Hammerstein models ignores the issue of noise outliers in actual industrial environments, this study improves the whale algorithm and constructs a Hammerstein model identification strategy for nonlinear systems under heavy-tailed noise using the improved whale algorithm. Results showed that it had a lower rank average and an average success rate of 95.65%. It found the global optimum when the number of iterations reached around 150 and had faster convergence speed and accuracy. In identifying Hammerstein model under heavy-tailed noise, the average prediction recognition accuracy of the improved whale algorithm was 92.38%, the determination coefficient was 0.89, the percentage fitting error was 0.03, and the system error was 0.02. This research achievement has certain value in the field of industrial control and can serve as a technical reference. |
| first_indexed | 2024-03-07T19:12:36Z |
| format | Article |
| id | doaj.art-bcb91cbe112848b79c75a5b4b4f4a292 |
| institution | Directory Open Access Journal |
| issn | 1687-0042 |
| language | English |
| last_indexed | 2024-03-07T19:12:36Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj.art-bcb91cbe112848b79c75a5b4b4f4a2922024-03-01T00:00:01ZengHindawi LimitedJournal of Applied Mathematics1687-00422024-01-01202410.1155/2024/5619098Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical TheoryLu Zhao0Jiangjun Liu1Yuan Li2Basic Teaching DepartmentBasic Teaching DepartmentBasic Teaching DepartmentWith the development of industrial demand, precise identification of system models is currently required in the field of industrial control, which limits the whale search algorithm. In response to the fact that whale optimization algorithms are prone to falling into local optima and the identification of important Hammerstein models ignores the issue of noise outliers in actual industrial environments, this study improves the whale algorithm and constructs a Hammerstein model identification strategy for nonlinear systems under heavy-tailed noise using the improved whale algorithm. Results showed that it had a lower rank average and an average success rate of 95.65%. It found the global optimum when the number of iterations reached around 150 and had faster convergence speed and accuracy. In identifying Hammerstein model under heavy-tailed noise, the average prediction recognition accuracy of the improved whale algorithm was 92.38%, the determination coefficient was 0.89, the percentage fitting error was 0.03, and the system error was 0.02. This research achievement has certain value in the field of industrial control and can serve as a technical reference.http://dx.doi.org/10.1155/2024/5619098 |
| spellingShingle | Lu Zhao Jiangjun Liu Yuan Li Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory Journal of Applied Mathematics |
| title | Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory |
| title_full | Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory |
| title_fullStr | Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory |
| title_full_unstemmed | Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory |
| title_short | Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory |
| title_sort | application of improved woa in hammerstein parameter resolution problems under advanced mathematical theory |
| url | http://dx.doi.org/10.1155/2024/5619098 |
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