A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach
A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduc...
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| Format: | Article |
| Language: | English |
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Wiley
2010-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2010/415895 |
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| _version_ | 1850228388681220096 |
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| author | Choon Ki Ahn |
| author_facet | Choon Ki Ahn |
| author_sort | Choon Ki Ahn |
| collection | DOAJ |
| description | A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL. |
| format | Article |
| id | doaj-art-204007c3ccf84b97b54d6151bdf3fe8c |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-204007c3ccf84b97b54d6151bdf3fe8c2025-08-20T02:04:33ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2010-01-01201010.1155/2010/415895415895A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI ApproachChoon Ki Ahn0Department of Automotive Engineering, Seoul National University of Science and Technology, 172 Gongneung 2-dong, Nowon-gu, Seoul 139-743, Republic of KoreaA new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.http://dx.doi.org/10.1155/2010/415895 |
| spellingShingle | Choon Ki Ahn A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach Discrete Dynamics in Nature and Society |
| title | A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach |
| title_full | A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach |
| title_fullStr | A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach |
| title_full_unstemmed | A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach |
| title_short | A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach |
| title_sort | new robust training law for dynamic neural networks with external disturbance an lmi approach |
| url | http://dx.doi.org/10.1155/2010/415895 |
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