State Estimation for Standard Neural Network Models with Time-Varying Delays
The paper deals with the issue of state estimation for standard neural network models with time-varying delays. A new augmented vector with the derivative of the state is introduced in the Lyapunov–Krasovskii functional. The state estimation criteria are obtained by constructing the suitable Lyapuno...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/4618101 |
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| _version_ | 1832554761642049536 |
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| author | Jin Zhu Tai-Fang Li Huanqing Wang |
| author_facet | Jin Zhu Tai-Fang Li Huanqing Wang |
| author_sort | Jin Zhu |
| collection | DOAJ |
| description | The paper deals with the issue of state estimation for standard neural network models with time-varying delays. A new augmented vector with the derivative of the state is introduced in the Lyapunov–Krasovskii functional. The state estimation criteria are obtained by constructing the suitable Lyapunov–Krasovskii functional; meanwhile, the observer gain and the controller gain are derived in terms of linear matrix inequality. The free matrix-based integral inequality is utilized to handle the integral terms, and the zero equation is added to the derivative of the Lyapunov–Krasovskii functional, which decreases the conservatism. The effectiveness and feasibility of the proposed methods are demonstrated by two numerical examples. |
| format | Article |
| id | doaj-art-950464fc9a0d4c399d7621f8c0c35c6c |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-950464fc9a0d4c399d7621f8c0c35c6c2025-02-03T05:50:39ZengWileyComplexity1099-05262022-01-01202210.1155/2022/4618101State Estimation for Standard Neural Network Models with Time-Varying DelaysJin Zhu0Tai-Fang Li1Huanqing Wang2College of Mathematics and ScienceCollege of Control Science and EngineeringCollege of Mathematics and ScienceThe paper deals with the issue of state estimation for standard neural network models with time-varying delays. A new augmented vector with the derivative of the state is introduced in the Lyapunov–Krasovskii functional. The state estimation criteria are obtained by constructing the suitable Lyapunov–Krasovskii functional; meanwhile, the observer gain and the controller gain are derived in terms of linear matrix inequality. The free matrix-based integral inequality is utilized to handle the integral terms, and the zero equation is added to the derivative of the Lyapunov–Krasovskii functional, which decreases the conservatism. The effectiveness and feasibility of the proposed methods are demonstrated by two numerical examples.http://dx.doi.org/10.1155/2022/4618101 |
| spellingShingle | Jin Zhu Tai-Fang Li Huanqing Wang State Estimation for Standard Neural Network Models with Time-Varying Delays Complexity |
| title | State Estimation for Standard Neural Network Models with Time-Varying Delays |
| title_full | State Estimation for Standard Neural Network Models with Time-Varying Delays |
| title_fullStr | State Estimation for Standard Neural Network Models with Time-Varying Delays |
| title_full_unstemmed | State Estimation for Standard Neural Network Models with Time-Varying Delays |
| title_short | State Estimation for Standard Neural Network Models with Time-Varying Delays |
| title_sort | state estimation for standard neural network models with time varying delays |
| url | http://dx.doi.org/10.1155/2022/4618101 |
| work_keys_str_mv | AT jinzhu stateestimationforstandardneuralnetworkmodelswithtimevaryingdelays AT taifangli stateestimationforstandardneuralnetworkmodelswithtimevaryingdelays AT huanqingwang stateestimationforstandardneuralnetworkmodelswithtimevaryingdelays |