Stabilization to Exponential Input-to-State Stability of a Class of Neural Networks with Delay by Observer-Based Aperiodic Intermittent Control
This study is devoted to investigating the stabilization to exponential input-to-state stability (ISS) of a class of neural networks with time delay and external disturbances under the observer-based aperiodic intermittent control (APIC). Compared with the general neural networks, the state of the n...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/9923792 |
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| author | Mengyue Li Biwen Li Yuan Wan |
| author_facet | Mengyue Li Biwen Li Yuan Wan |
| author_sort | Mengyue Li |
| collection | DOAJ |
| description | This study is devoted to investigating the stabilization to exponential input-to-state stability (ISS) of a class of neural networks with time delay and external disturbances under the observer-based aperiodic intermittent control (APIC). Compared with the general neural networks, the state of the neural network investigated is not yet fully available. Correspondingly, an observer-based APIC is constructed, and moreover, neither the observer nor the controller requires the information of time delay. Then, the stabilization to exponential ISS of the neural network is realized severally by the observer-based time-triggered APIC (T-APIC) and the observer-based event-triggered APIC (E-APIC), and corresponding criteria are given. Furthermore, the minimum activation time rate (MATR) of the observer-based T-APIC and the observer-based E-APIC is estimated, respectively. Finally, a numerical example is given, which not only verifies the effectiveness of our results but also shows that the observer-based E-APIC is superior to the observer-based T-APIC and the observer-based periodic intermittent control (PIC) in control times and the minimum activation time rate, and the function of the observer-based T-APIC is also better than the observer-based PIC. |
| format | Article |
| id | doaj-art-5b1af825d414401f908273e2360aadcf |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-5b1af825d414401f908273e2360aadcf2025-02-03T01:25:10ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/99237929923792Stabilization to Exponential Input-to-State Stability of a Class of Neural Networks with Delay by Observer-Based Aperiodic Intermittent ControlMengyue Li0Biwen Li1Yuan Wan2College of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, ChinaCollege of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, ChinaKey Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518034, ChinaThis study is devoted to investigating the stabilization to exponential input-to-state stability (ISS) of a class of neural networks with time delay and external disturbances under the observer-based aperiodic intermittent control (APIC). Compared with the general neural networks, the state of the neural network investigated is not yet fully available. Correspondingly, an observer-based APIC is constructed, and moreover, neither the observer nor the controller requires the information of time delay. Then, the stabilization to exponential ISS of the neural network is realized severally by the observer-based time-triggered APIC (T-APIC) and the observer-based event-triggered APIC (E-APIC), and corresponding criteria are given. Furthermore, the minimum activation time rate (MATR) of the observer-based T-APIC and the observer-based E-APIC is estimated, respectively. Finally, a numerical example is given, which not only verifies the effectiveness of our results but also shows that the observer-based E-APIC is superior to the observer-based T-APIC and the observer-based periodic intermittent control (PIC) in control times and the minimum activation time rate, and the function of the observer-based T-APIC is also better than the observer-based PIC.http://dx.doi.org/10.1155/2021/9923792 |
| spellingShingle | Mengyue Li Biwen Li Yuan Wan Stabilization to Exponential Input-to-State Stability of a Class of Neural Networks with Delay by Observer-Based Aperiodic Intermittent Control Discrete Dynamics in Nature and Society |
| title | Stabilization to Exponential Input-to-State Stability of a Class of Neural Networks with Delay by Observer-Based Aperiodic Intermittent Control |
| title_full | Stabilization to Exponential Input-to-State Stability of a Class of Neural Networks with Delay by Observer-Based Aperiodic Intermittent Control |
| title_fullStr | Stabilization to Exponential Input-to-State Stability of a Class of Neural Networks with Delay by Observer-Based Aperiodic Intermittent Control |
| title_full_unstemmed | Stabilization to Exponential Input-to-State Stability of a Class of Neural Networks with Delay by Observer-Based Aperiodic Intermittent Control |
| title_short | Stabilization to Exponential Input-to-State Stability of a Class of Neural Networks with Delay by Observer-Based Aperiodic Intermittent Control |
| title_sort | stabilization to exponential input to state stability of a class of neural networks with delay by observer based aperiodic intermittent control |
| url | http://dx.doi.org/10.1155/2021/9923792 |
| work_keys_str_mv | AT mengyueli stabilizationtoexponentialinputtostatestabilityofaclassofneuralnetworkswithdelaybyobserverbasedaperiodicintermittentcontrol AT biwenli stabilizationtoexponentialinputtostatestabilityofaclassofneuralnetworkswithdelaybyobserverbasedaperiodicintermittentcontrol AT yuanwan stabilizationtoexponentialinputtostatestabilityofaclassofneuralnetworkswithdelaybyobserverbasedaperiodicintermittentcontrol |