Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays
A class of dynamical neural network models with time-varying delays is considered. By employing the Lyapunov-Krasovskii functional method and linear matrix inequalities (LMIs) technique, some new sufficient conditions ensuring the input-to-state stability (ISS) property of the nonlinear network syst...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2012/372324 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832561663051563008 |
|---|---|
| author | Weisong Zhou Zhichun Yang |
| author_facet | Weisong Zhou Zhichun Yang |
| author_sort | Weisong Zhou |
| collection | DOAJ |
| description | A class of dynamical neural network models with time-varying delays is considered. By employing the Lyapunov-Krasovskii functional method and linear matrix inequalities (LMIs) technique, some new sufficient conditions ensuring the input-to-state stability (ISS) property of the nonlinear network systems are obtained. Finally, numerical examples are provided to illustrate the efficiency of the derived results. |
| format | Article |
| id | doaj-art-c36a7b6757134db78d72e815f742256d |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-c36a7b6757134db78d72e815f742256d2025-02-03T01:24:31ZengWileyAbstract and Applied Analysis1085-33751687-04092012-01-01201210.1155/2012/372324372324Input-to-State Stability for Dynamical Neural Networks with Time-Varying DelaysWeisong Zhou0Zhichun Yang1Department of Mathematics, Chongqing Normal University, Chongqing 400047, ChinaDepartment of Mathematics, Chongqing Normal University, Chongqing 400047, ChinaA class of dynamical neural network models with time-varying delays is considered. By employing the Lyapunov-Krasovskii functional method and linear matrix inequalities (LMIs) technique, some new sufficient conditions ensuring the input-to-state stability (ISS) property of the nonlinear network systems are obtained. Finally, numerical examples are provided to illustrate the efficiency of the derived results.http://dx.doi.org/10.1155/2012/372324 |
| spellingShingle | Weisong Zhou Zhichun Yang Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays Abstract and Applied Analysis |
| title | Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays |
| title_full | Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays |
| title_fullStr | Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays |
| title_full_unstemmed | Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays |
| title_short | Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays |
| title_sort | input to state stability for dynamical neural networks with time varying delays |
| url | http://dx.doi.org/10.1155/2012/372324 |
| work_keys_str_mv | AT weisongzhou inputtostatestabilityfordynamicalneuralnetworkswithtimevaryingdelays AT zhichunyang inputtostatestabilityfordynamicalneuralnetworkswithtimevaryingdelays |