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

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.