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研究了具有连续分布式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。在对神经元的激励函数的两种较宽松的假设下,应用Lyapunov泛函法及不等式分析技巧获得了全局渐近稳定性的两个新的充分条件。该结果推广了有关文献中已有的结论,数值仿真的例子证明了其有效性。
The global stability of the equilibrium point of bidirectional associative memory neural networks with continuous distributed delays is studied. Two new sufficient conditions for global asymptotic stability of Lyapunov functional and inequality analysis techniques are obtained under the two loose assumptions of the neuron ’s excitation function. This result generalizes the existing conclusion in the literature, and the numerical simulation example proves its validity.