H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks
This paper presents a H-infinite state estimator for Takagi-Sugeno fuzzy delayed Hopfield neural networks. Based on Lyapunov-Krasovskii stability approach, a delay-dependent criterion is proposed to ensure that the resulting estimation error system is asymptotically stable with a guaranteed H perfor...
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| Format: | Article |
| Language: | English |
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Springer
2011-10-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/2379.pdf |
| _version_ | 1818233482152771584 |
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| author | Choon Ki Ahn |
| author_facet | Choon Ki Ahn |
| author_sort | Choon Ki Ahn |
| collection | DOAJ |
| description | This paper presents a H-infinite state estimator for Takagi-Sugeno fuzzy delayed Hopfield neural networks. Based on Lyapunov-Krasovskii stability approach, a delay-dependent criterion is proposed to ensure that the resulting estimation error system is asymptotically stable with a guaranteed H performance. The proposed H state estimator can be realized by solving a linear matrix inequality (LMI) problem. An illustrative numerical example is given to verify the effectiveness of the proposed H-infinite state estimator. |
| first_indexed | 2024-12-12T11:22:53Z |
| format | Article |
| id | doaj.art-4a23a1d2aabf43fdadc8bf96937562bc |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-12T11:22:53Z |
| publishDate | 2011-10-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-4a23a1d2aabf43fdadc8bf96937562bc2022-12-22T00:25:59ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832011-10-014510.2991/ijcis.2011.4.5.11H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural NetworksChoon Ki AhnThis paper presents a H-infinite state estimator for Takagi-Sugeno fuzzy delayed Hopfield neural networks. Based on Lyapunov-Krasovskii stability approach, a delay-dependent criterion is proposed to ensure that the resulting estimation error system is asymptotically stable with a guaranteed H performance. The proposed H state estimator can be realized by solving a linear matrix inequality (LMI) problem. An illustrative numerical example is given to verify the effectiveness of the proposed H-infinite state estimator.https://www.atlantis-press.com/article/2379.pdfH-infinite state estimationTakagi-Sugeno fuzzy Hopfield neural networkslinear matrix inequality |
| spellingShingle | Choon Ki Ahn H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks International Journal of Computational Intelligence Systems H-infinite state estimation Takagi-Sugeno fuzzy Hopfield neural networks linear matrix inequality |
| title | H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks |
| title_full | H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks |
| title_fullStr | H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks |
| title_full_unstemmed | H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks |
| title_short | H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks |
| title_sort | h infinite state estimation for takagi sugeno fuzzy delayed hopfield neural networks |
| topic | H-infinite state estimation Takagi-Sugeno fuzzy Hopfield neural networks linear matrix inequality |
| url | https://www.atlantis-press.com/article/2379.pdf |
| work_keys_str_mv | AT choonkiahn hinfinitestateestimationfortakagisugenofuzzydelayedhopfieldneuralnetworks |