Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay
This paper investigates the problem of adaptive event-triggered synchronization for uncertain FNNs subject to double deception attacks and time-varying delay. During network transmission, a practical deception attack phenomenon in FNNs should be considered; that is, we investigated the situation in...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/10/1291 |
| _version_ | 1797514653008396288 |
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| author | Zhuan Shen Fan Yang Jing Chen Jingxiang Zhang Aihua Hu Manfeng Hu |
| author_facet | Zhuan Shen Fan Yang Jing Chen Jingxiang Zhang Aihua Hu Manfeng Hu |
| author_sort | Zhuan Shen |
| collection | DOAJ |
| description | This paper investigates the problem of adaptive event-triggered synchronization for uncertain FNNs subject to double deception attacks and time-varying delay. During network transmission, a practical deception attack phenomenon in FNNs should be considered; that is, we investigated the situation in which the attack occurs via both communication channels, from S-C and from C-A simultaneously, rather than considering only one, as in many papers; and the double attacks are described by high-level Markov processes rather than simple random variables. To further reduce network load, an advanced AETS with an adaptive threshold coefficient was first used in FNNs to deal with deception attacks. Moreover, given the engineering background, uncertain parameters and time-varying delay were also considered, and a feedback control scheme was adopted. Based on the above, a unique closed-loop synchronization error system was constructed. Sufficient conditions that guarantee the stability of the closed-loop system are ensured by the Lyapunov-Krasovskii functional method. Finally, a numerical example is presented to verify the effectiveness of the proposed method. |
| first_indexed | 2024-03-10T06:34:39Z |
| format | Article |
| id | doaj.art-5d4724ec6dfa4c8794a7a394d50380ed |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T06:34:39Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-5d4724ec6dfa4c8794a7a394d50380ed2023-11-22T18:10:45ZengMDPI AGEntropy1099-43002021-09-012310129110.3390/e23101291Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying DelayZhuan Shen0Fan Yang1Jing Chen2Jingxiang Zhang3Aihua Hu4Manfeng Hu5School of Science, Jiangnan University, Wuxi 214122, ChinaSchool of Science, Jiangnan University, Wuxi 214122, ChinaSchool of Science, Jiangnan University, Wuxi 214122, ChinaSchool of Science, Jiangnan University, Wuxi 214122, ChinaSchool of Science, Jiangnan University, Wuxi 214122, ChinaSchool of Science, Jiangnan University, Wuxi 214122, ChinaThis paper investigates the problem of adaptive event-triggered synchronization for uncertain FNNs subject to double deception attacks and time-varying delay. During network transmission, a practical deception attack phenomenon in FNNs should be considered; that is, we investigated the situation in which the attack occurs via both communication channels, from S-C and from C-A simultaneously, rather than considering only one, as in many papers; and the double attacks are described by high-level Markov processes rather than simple random variables. To further reduce network load, an advanced AETS with an adaptive threshold coefficient was first used in FNNs to deal with deception attacks. Moreover, given the engineering background, uncertain parameters and time-varying delay were also considered, and a feedback control scheme was adopted. Based on the above, a unique closed-loop synchronization error system was constructed. Sufficient conditions that guarantee the stability of the closed-loop system are ensured by the Lyapunov-Krasovskii functional method. Finally, a numerical example is presented to verify the effectiveness of the proposed method.https://www.mdpi.com/1099-4300/23/10/1291uncertain fractional order neural networkadaptive event-triggered schemedouble deception attackstime-varying delay |
| spellingShingle | Zhuan Shen Fan Yang Jing Chen Jingxiang Zhang Aihua Hu Manfeng Hu Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay Entropy uncertain fractional order neural network adaptive event-triggered scheme double deception attacks time-varying delay |
| title | Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay |
| title_full | Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay |
| title_fullStr | Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay |
| title_full_unstemmed | Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay |
| title_short | Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay |
| title_sort | adaptive event triggered synchronization of uncertain fractional order neural networks with double deception attacks and time varying delay |
| topic | uncertain fractional order neural network adaptive event-triggered scheme double deception attacks time-varying delay |
| url | https://www.mdpi.com/1099-4300/23/10/1291 |
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