Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term
The stability of a class of static interval neural networks with time delay in the leakage term is investigated. By using the method of M-matrix and the technique of delay differential inequality, we obtain some sufficient conditions ensuring the global exponential robust stability of the networks....
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/972608 |
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| _version_ | 1832556112681893888 |
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| author | Guiying Chen Linshan Wang |
| author_facet | Guiying Chen Linshan Wang |
| author_sort | Guiying Chen |
| collection | DOAJ |
| description | The stability of a class of static interval neural networks with time delay in the leakage term is investigated. By using the method of M-matrix and the technique of delay differential inequality, we obtain some sufficient conditions ensuring the global exponential robust stability of the networks. The results in this paper extend the corresponding conclusions without leakage delay. An example is given to illustrate the effectiveness of the obtained results. |
| format | Article |
| id | doaj-art-4672993750704c7bbcc4c1a8330ee63f |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-4672993750704c7bbcc4c1a8330ee63f2025-02-03T05:46:21ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/972608972608Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage TermGuiying Chen0Linshan Wang1School of Mathematical Science, Ocean University of China, Qingdao 266100, ChinaSchool of Mathematical Science, Ocean University of China, Qingdao 266100, ChinaThe stability of a class of static interval neural networks with time delay in the leakage term is investigated. By using the method of M-matrix and the technique of delay differential inequality, we obtain some sufficient conditions ensuring the global exponential robust stability of the networks. The results in this paper extend the corresponding conclusions without leakage delay. An example is given to illustrate the effectiveness of the obtained results.http://dx.doi.org/10.1155/2014/972608 |
| spellingShingle | Guiying Chen Linshan Wang Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term Journal of Applied Mathematics |
| title | Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term |
| title_full | Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term |
| title_fullStr | Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term |
| title_full_unstemmed | Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term |
| title_short | Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term |
| title_sort | global exponential robust stability of static interval neural networks with time delay in the leakage term |
| url | http://dx.doi.org/10.1155/2014/972608 |
| work_keys_str_mv | AT guiyingchen globalexponentialrobuststabilityofstaticintervalneuralnetworkswithtimedelayintheleakageterm AT linshanwang globalexponentialrobuststabilityofstaticintervalneuralnetworkswithtimedelayintheleakageterm |