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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 .
By constructing an appropriate Lyapunov functional and applying analytical techniques of inequality, we study the global stability of the equilibrium point of bidirectional associative memory neural networks with time delay. Under the assumption that the neuron excitation function is more relaxed, Lipschitz condition), a new criterion for global asymptotic stability is obtained.The results can be applied to the design of a neural network that includes a more extensive class of neuron excitation functions than non-Lipschitz.