Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay
This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to con...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/391282 |
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| _version_ | 1832554328572821504 |
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| author | Lei Ding Hong-Bing Zeng Wei Wang Fei Yu |
| author_facet | Lei Ding Hong-Bing Zeng Wei Wang Fei Yu |
| author_sort | Lei Ding |
| collection | DOAJ |
| description | This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods. |
| format | Article |
| id | doaj-art-f11fd8a13bce4f3ea2413126cbf7f31a |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-f11fd8a13bce4f3ea2413126cbf7f31a2025-02-03T05:51:50ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/391282391282Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying DelayLei Ding0Hong-Bing Zeng1Wei Wang2Fei Yu3School of Information Science and Engineering, Jishou University, Jishou 416000, ChinaSchool of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaHunan Railway Professional Technology College, Zhuzhou 412001, ChinaJiangsu Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Soochow 215006, ChinaThis paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods.http://dx.doi.org/10.1155/2014/391282 |
| spellingShingle | Lei Ding Hong-Bing Zeng Wei Wang Fei Yu Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay The Scientific World Journal |
| title | Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay |
| title_full | Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay |
| title_fullStr | Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay |
| title_full_unstemmed | Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay |
| title_short | Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay |
| title_sort | improved stability criteria of static recurrent neural networks with a time varying delay |
| url | http://dx.doi.org/10.1155/2014/391282 |
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