Fault detection filtering for MNNs with dynamic quantization and improved protocol

This paper concerns the fault detection filtering problem for discrete-time memristive neural networks with mixed time delays. An improved dynamic event-triggering protocol, whose multiple threshold functions are dynamically adjustable, is presented to decrease the utilization of limited resources and achieve desired performance. Two mutually independent Bernoulli variables are given to depicting the randomly occurring cyber-attacks. Meanwhile, a dynamic quantizer is established to account for restricted bandwidth efficiently. Based on the Lyapunov theory, sufficient conditions are derived to ensure the filtering error system is exponential mean square stable and desired performance. In the end, a numerical example is provided to verify the effectiveness of the proposed methodology.