Protocol-based fault detection filtering for memristive neural networks with dynamic quantization

This study examines the issue of event-triggered fault detection filtering for memristive neural networks with dynamic quantization in discrete-time domain. To facilitate digital transmissions, the system output undergoes dynamic quantized prior to transmission. Beyond the reporting event-triggered protocol, a novel event-triggered protocol is enforced, associating with dynamic quantization parameter, fault occurrence probability and network bandwidth utilization rate, to skillfully schedule the transmission frequency. A random variable that follows a binary Markov process, instead of a Bernoulli distribution, is presented to characterize the dynamic impact of denial-of-service attacks. On account of hidden Markov model and Lyapunov theory, an asynchronous filter framework is formulated to ensure stochastically stable of resulting filtering error systems. Ultimately, a simulation example is conducted to validate the usefulness of the developed methodology. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.