时滞双向联想记忆神经网络的全局稳定性

通过构造一个合适的Lyapunov泛函及应用不等式的分析技巧研究了具有时滞的双向联想记忆 神经网络的平衡点的全局稳定性问题-在对神经元激励函数较宽松的假设条件下（可以不满 足Lipschitz条件），获得了一个新的保证全局渐近稳定性的判定准则-结果可应用于包含非 Lipschitz的一类更加广泛的神经元激励函数的神经网络的设计中-关键词：Lyapunov泛函时滞双向联想记忆神经网络全局渐近稳定性

Global stability of bidirectional associative memory neural networks with dela ys

The stability of the equilibrium point of bidirectional associative memory (BAM) neural networks with delays is studied by constructing a suitable Lyapunov functional and combining with some inequality analysis techniques- On the assumption that the activation functions of neurons are less restrictive than those in t he literature (which may not satisfy Lipschitz condition), a new sufficient cond ition ensuring the global asymptotic stability of BAM neural networks with delay s is derived- The results presented here can be applied to the design of a wider class of neural networks including non-Lipschitz activation functions of neuron s-