This analysis reveals event-triggered synchronization in semi-Markov neural networks, indicating enhanced fault tolerance.
This paper studies discrete-time semi-Markov neural networks (NNs) and proposes a finite-time γ-dissipative fault estimate (FE) and fault-tolerant synchronization control (FTSC) with the event-triggered method. Based on the intermediate variable introduced, an FE scheme is presented to obtain estimate information of faults, states, and intermediate variables. According to the event-triggered condition designed, an FTSC protocol is proposed so that the error system between master system and slave system is stochasticslly synchronized within finite time and meets γ-dissipativity. An example shows the validity of this proposed scheme.
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Zhu et al. (2025) studied this question.
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