As is known to all that the Lyapunov‐Krasovskii functional (LKF) method plays a significant role in deriving exponential stability criteria of neural networks with a time‐varying delay. However, when the LKF method is adopted, the condition that a functional is required for a neural network with a delay varying in a delay interval is so strong that it may be hard to be satisfied and lead to a conservative criterion. Therefore, a generalized LKF method is proposed by weakening the strong condition in this paper. Then, new exponential stability criteria are derived via applying the proposed method. Finally, the effectiveness of the derived criteria is verified by two numerical examples.

中文翻译：

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时变时滞神经网络的指数稳定性的广义Lyapunov-Krasovskii泛函分析

众所周知，Lyapunov-Krasovskii函数（LKF）方法在推导具有时变时延的神经网络的指数稳定性标准中起着重要作用。然而，当采用LKF方法时，具有在延迟间隔中变化的延迟的神经网络需要功能的条件是如此强，以至于可能难以满足并且导致保守的标准。因此，通过弱化强条件，提出了一种广义的LKF方法。然后，通过应用所提出的方法导出新的指数稳定性准则。最后，通过两个数值例子验证了所推导标准的有效性。