Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol

作者：An Lin, Jun Cheng, Leszek Rutkowski, Shiping Wen, Mengzhuo Luo, Jinde Cao · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2022 · DOI：10.1109/tnnls.2022.3155149 · 被引用次数：29 · 研究领域：Advanced Memory and Neural Computing、Neural Networks Stability and Synchronization、stochastic dynamics and bifurcation

This article studies the asynchronous fault detection filter problem for discrete-time memristive neural networks with a stochastic communication protocol (SCP) and denial-of-service attacks. Aiming at alleviating the occurrence of network-induced phenomena, a dwell-time-based SCP is scheduled to coordinate the packet transmission between sensors and filter, whose deterministic switching signal arranges the proper feedback switching information among the homogeneous Markov processes (HMPs) for different scenarios. A variable obeying the Bernoulli distribution is proposed to characterize the randomly occurring denial-of-service attacks, in which the attack rate is uncertain. More specifically, both dwell-time-based SCP and denial-of-service attacks are modeled by means of compensation strategy. In light of the mode mismatches between data transmission and filter, a hidden Markov model (HMM) is adopted to describe the asynchronous fault detection filter. Consequently, sufficient conditions of stochastic stability of memristive neural networks are devised with the assistance of Lyapunov theory. In the end, a numerical example is applied to show the effectiveness of the theoretical method.