Local Synchronization Criteria of Markovian Nonlinearly Coupled Neural Networks With Uncertain and Partially Unknown Transition Rates
研究了转移速率不确定或未知的马尔可夫非线性耦合神经网络的局部同步问题，利用Lyapunov-Krasovskii泛函和新的积分不等式得到保守性更低的同步准则，并通过仿真验证了有效性。
In this paper, the local synchronization problem of Markovian nonlinearly coupled neural networks with uncertain and partially unknown transition rates is investigated. Each transition rate in this Markovian nonlinearly coupled neural networks model is uncertain or completely unknown because the complete knowledge on the transition rates is difficult and the cost is probably high. By applying the Lyapunov-Krasovskii functional, a new integral inequality combining with free-matrix-based integral inequality and further improved integral inequality, the less conservative local synchronization criteria are obtained. The new delay-dependent local synchronization criteria containing the bounds of delay and delay derivative are given in terms of linear matrix inequalities. Finally, a simulation example is provided to illustrate the effectiveness of the proposed method.