Exponential Stability of Switched Delayed Memristive Neural Networks with AED-ADT Method and False Data Injection Attacks

This paper focuses on the exponential stability of switched memristive neural networks (SMNNs) with leakage and discrete delays subject to false data injection (FDI) attacks. First, this paper designs the SMNNs model with leakage and discrete delays based on the decay process of neurons and the limitations of signal transmission. Second, an admissible edge-dependent average dwell time (AED-ADT) switching law is introduced, which can utilize the mode information and reflect the switching direction between modes. Then, a mode-dependent impulsive controller is proposed to stabilize the SMNNs subject to FDI attacks. By constructing Lyapunov-Krasovskii functionals and using inequality techniques, the exponential stability criterion is derived. Finally, a numerical example is given to illustrate the validity of the obtained results.