Global Attractivity and Global Exponential Stability for Delayed Hopfield Neural Network Models
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摘要: 对具有时滞的Hopfield神经网络模型,在非线性神经元激励函数是Lipschitz连续(而非已有的大部分文献中假设是Sigmoid函数)的条件下,通过构造适当的泛函,给出了这类模型全局吸引和平衡点全局指数稳定的易于验证的充分条件。Abstract: Some global properties such as global attractivity and global exponential stability for delayed Hopfield neural networks model,under the weaker assumptions on nonlinear activation functions,are concerned.By constructing suitable Liapunov function,some simpler criteria for global attractivity and global exponential stability for Hopfield continuous neural networks with time delays are presented.
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Key words:
- neural networks /
- global attractivity /
- global exponential stability
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