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摘要: 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-
Abstract: 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-
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