时变时滞双向联想记忆神经网络的鲁棒稳定性

通过构造适当的Lyapunov泛函分析了一类时变时滞双向联想记忆神经网络的平衡点稳定性问题。不要求激励函数的单调性和可微性,得到了保证时变时滞双向联想记忆神经网络的平衡点全局鲁棒渐近稳定的两个新判据。所得到的结果能够表示成线性矩阵不等式形式,进而易于用内点算法等方法来验证。通过仿真例子与其他文献中的一些结果进行比较,表明了本文所得结果的有效性。

Robust stability of bi-directional associate memory neural networks with time varying delays

Based on the construction of Lyapunov function, global stability problems were studied for bi-directional associate memory neural networks with time varying delays. Without monotonicity and differentiation assumptions on activation function, two sufficient conditions were derived for the global robust stability of equilibrium point. The obtained results possess a linear matrix inequality structure and can be efficiently solved by using interior-point algorithms. Comparison between our results and the previous ones through a numerical example was conducted, which implies the effectiveness of the proposed results.