H∞/Passive Synchronization of Semi-Markov Jump Neural Networks Subject to Hybrid Attacks via an Activation Function Division Approach

• Published in: Journal of systems science and complexity Vol. 37; no. 3; pp. 1023 - 1036
• Main Authors: Zhang, Ziwei, Shen, Hao, Su, Lei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024; Springer Nature B.V
• Subjects: Complex Systems; Control; Criteria; Denial of service attacks; H-infinity control; Linear matrix inequalities; Mathematical analysis; Mathematics; Mathematics and Statistics; Mathematics of Computing; Neural networks; Operations Research/Decision Theory; Statistics; Synchronism; Systems Theory; semi-Markov jump neural networks; deception attacks; Activation function division approach; denial-of-service attacks; passive synchronization
• ISSN: 1009-6124; 1559-7067
• DOI: 10.1007/s11424-024-3049-8

In this work, an H ∞ /passive-based secure synchronization control problem is investigated for continuous-time semi-Markov neural networks subject to hybrid attacks, in which hybrid attacks are the combinations of denial-of-service attacks and deception attacks, and they are described by two groups of independent Bernoulli distributions. On this foundation, via the Lyapunov stability theory and linear matrix inequality technology, the H ∞ /passive-based performance criteria for semi-Markov jump neural networks are obtained. Additionally, an activation function division approach for neural networks is adopted to further reduce the conservatism of the criteria. Finally, a simulation example is provided to verify the validity and feasibility of the proposed method.