Adaptive event-triggered H filtering for a class of discrete-time neural networks under deception attacks
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TP13;TN713

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    Abstract:

    The H filter design is addressed for the discrete-time neural networks subject to deception attacks.Considering the information exchange between the controlled system and the filter over the shared communication network with limited bandwidth and vulnerability to external network attacks,an adaptive event triggering mechanism (AETM) is proposed to reduce the communication burden of data transmission.In addition,due to the open access and interconnection of the communication network,the information transmitted via the shared communication network may be tampered by the fabricated information injected by the attacker.On this basis,by using Lyapunov-Krasovski functional and linear matrix inequality,the sufficient conditions for the asymptotic stability of the filtering error system are given,and the H filter satisfying the preset performance is designed.Finally,a simulation example is provided to verify the effectiveness of the proposed method.

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WANG Jinxia, GAO Jinfeng, TAN Tian. Adaptive event-triggered H filtering for a class of discrete-time neural networks under deception attacks[J]. Journal of Nanjing University of Information Science & Technology,2021,13(1):102-110

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  • Received:October 18,2020
  • Online: March 31,2021
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