The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise
Abstract The characteristic of the external noise has significant influences on system modelling and identification, and the assumption that the noise follows the Gaussian distribution may be invalid due to realistic reasons. This paper discusses the identification issue of Hammerstein non‐linear sy...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-04-01
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| Series: | IET Control Theory & Applications |
| Online Access: | https://doi.org/10.1049/cth2.12097 |
| _version_ | 1828396193700380672 |
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| author | Xuehai Wang Feng Ding |
| author_facet | Xuehai Wang Feng Ding |
| author_sort | Xuehai Wang |
| collection | DOAJ |
| description | Abstract The characteristic of the external noise has significant influences on system modelling and identification, and the assumption that the noise follows the Gaussian distribution may be invalid due to realistic reasons. This paper discusses the identification issue of Hammerstein non‐linear systems with non‐Gaussian noise and presents a robust gradient algorithm. The algorithm is derived based on the logarithmic cost function of continuous mixed p‐norm of prediction errors, which takes into account each p‐norm of errors for 1⩽p⩽2. The gain at each recursive step adapts to the data quality so that the algorithm has good robustness to non‐Gaussian noise. To improve the estimation accuracy, a robust multi‐innovation gradient algorithm is proposed by using the multi‐innovation identification theory. Two examples are provided to exhibit the validity of the proposed algorithms. |
| first_indexed | 2024-12-10T08:26:12Z |
| format | Article |
| id | doaj.art-c24d81566e8a42b68037f5e63172987d |
| institution | Directory Open Access Journal |
| issn | 1751-8644 1751-8652 |
| language | English |
| last_indexed | 2024-12-10T08:26:12Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj.art-c24d81566e8a42b68037f5e63172987d2022-12-22T01:56:14ZengWileyIET Control Theory & Applications1751-86441751-86522021-04-01157989100210.1049/cth2.12097The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noiseXuehai Wang0Feng Ding1School of Mathematics and Statistics Xinyang Normal University PR ChinaCollege of Automation and Electronic Engineering Qingdao University of Science and Technology PR ChinaAbstract The characteristic of the external noise has significant influences on system modelling and identification, and the assumption that the noise follows the Gaussian distribution may be invalid due to realistic reasons. This paper discusses the identification issue of Hammerstein non‐linear systems with non‐Gaussian noise and presents a robust gradient algorithm. The algorithm is derived based on the logarithmic cost function of continuous mixed p‐norm of prediction errors, which takes into account each p‐norm of errors for 1⩽p⩽2. The gain at each recursive step adapts to the data quality so that the algorithm has good robustness to non‐Gaussian noise. To improve the estimation accuracy, a robust multi‐innovation gradient algorithm is proposed by using the multi‐innovation identification theory. Two examples are provided to exhibit the validity of the proposed algorithms.https://doi.org/10.1049/cth2.12097 |
| spellingShingle | Xuehai Wang Feng Ding The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise IET Control Theory & Applications |
| title | The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise |
| title_full | The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise |
| title_fullStr | The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise |
| title_full_unstemmed | The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise |
| title_short | The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise |
| title_sort | robust multi innovation estimation algorithm for hammerstein non linear systems with non gaussian noise |
| url | https://doi.org/10.1049/cth2.12097 |
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