Exponential Stability, Passivity, and Dissipativity Analysis of Generalized Neural Networks With Mixed Time-Varying Delays
研究了含混合时变时滞的广义神经网络的指数稳定性、无源性和耗散性，通过构造Lyapunov-Krasovskii泛函和新的加权积分不等式，以线性矩阵不等式形式给出了判据，并用数值例子验证了方法的优越性。
In this paper, we analyze the exponential stability, passivity, and (Q, G, R)-γ-dissipativity of generalized neural networks (GNNs) including mixed time-varying delays in state vectors. Novel exponential stability, passivity, and (Q, G, R)-γ-dissipativity criteria are developed in the form of linear matrix inequalities for continuous-time GNNs by constructing an appropriate Lyapunov-Krasovskii functional (LKF) and applying a new weighted integral inequality for handling integral terms in the time derivative of the established LKF for both single and double integrals. Some special cases are also discussed. The superiority of employing the method presented in this paper over some existing methods is verified by numerical examples.