Secure state estimation of memristive neural networks with dynamic self-triggered strategy subject to deception attacks

This paper is dedicated to addressing state estimation for memristive neural networks (MNNs) featuring dynamic self-triggered mechanisms (DSTM) subject to deception attacks (DA). Taking into account the constrained channel bandwidth, the data sampling controller by dynamic self-triggering is proposed for measurement output. The network transmission of data among sensor and estimator is susceptible to deception attacks, and a corresponding state estimator is developed. Utilizing Lyapunov stability theory, it is demonstrated that the state error system is exponentially ultimately bounded in the mean square, and the dynamic self-triggered strategy avoids Zeno behavior. Furthermore, the estimation gains are obtained using a linear matrix inequality (LMI) approach. Lastly, simulated examples are provided to demonstrate the efficacy of the proposed approach.