Synchronization analysis of discrete-time coupled complex valued Markov jump neural network systems with two unknown transition probabilities’ Markov chains
H.J. Xing, Hongqian Lu · 2024
In this paper, we provide a array of discrete-time coupled complex-valued markov jump neural networks (CMNNs) with two markov chains of partially unknown transition probabilities and distributed delays. First, for the system self-synchronization problem of the coupled CMNNs, by constructing the Lyapunov energy function and the Kronecker product, the system coupling force are used to propose a synchronization criterion that enables the coupled CMNNs to satisfy the mean-square asymptotic synchronization. Next, to validate the theories discussed in the paper, two examples of numeric experimental simulations are used.