This analysis demonstrates effective fault-tolerant synchronization in memristive neural networks, suggesting new approaches to handle system failures.
In this article, the synchronization problem of memristive neural networks (MNNs) subjected to multiple failures is investigated. First, a general form of fault model is introduced into the MNNs, which can represent and summarize various process faults, actuator faults, and their coupling. Subsequently, with the help of designing intermediate variables, two types of fault function observers based on state feedback and output feedback are constructed, and their effectiveness is verified through a generalization of Halanay-type inequalities. Then, based on the designed observers and the event-triggered strategy, two classes of fault-tolerant synchronization schemes are designed for the considered MNNs. By adjusting the controller parameter conditions, finite-time and fixed-time synchronization or quasi-synchronization of the considered MNNs system can be achieved, respectively. Finally, the effectiveness of the provided fault observers and synchronization strategies is verified through simulation and comparison experiments.
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Wang et al. (2025) studied this question.
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