H ∞ state estimation for memristive neural networks with randomly occurring DoS attacks

• Published in: Systems science & control engineering Vol. 10; no. 1; pp. 154 - 165
• Main Authors: Tao, Huimin, Tan, Hailong, Chen, Qiwen, Liu, Hongjian, Hu, Jun
• Format: Journal Article
• Language: English
• Published: Macclesfield Taylor & Francis 31-12-2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Communication; Denial of service attacks; Discrete-time memristive neural networks; Dwell time; extended dissipative state estimation; Liapunov functions; Linear matrix inequalities; Mathematical analysis; Neural networks; Open access publishing; randomly occurring denial-of-service attacks; State estimation; Systems science; Systems stability; time-varying delays
• ISSN: 2164-2583; 2164-2583
• DOI: 10.1080/21642583.2022.2048322

This study deals with the problem of the
state estimation for discrete-time memristive neural networks with time-varying delays, where the output is subject to randomly occurring denial-of-service attacks. The average dwell time is used to describe the attack rules, which makes the randomly occurring denial-of-service attack more universal. The main purpose of the addressed issue is to contribute with a state estimation method, so that the dynamics of the error system is exponentially mean-square stable and satisfies a prescribed
disturbance attenuation level. Sufficient conditions for the solvability of such a problem are established by employing the Lyapunov function and stochastic analysis techniques. Estimator gain is described explicitly in terms of certain linear matrix inequalities. Finally, the effectiveness of the proposed state estimation scheme is proved by a numerical example.