时滞Cohen-Grossberg神经网络p阶矩ψ(t)稳定性研究
首发时间:2018-05-04
摘要:神经网络在信息和图像处理、模式的识别、联想记忆、组合的最优化等领域都有着成功的应用,也引起了国内外学者的广泛关注。然而,在神经网络中,时滞的出现可能会影响到整个神经网络的稳定性,研究时滞随机神经网络的稳定性有着重要的理论意义。本文研究了随机时滞Cohen-Grossberg神经网络的稳定性。利用不等式技巧,鞅收敛定理及李雅普诺夫函数,建立了时滞Cohen-Grossberg神经网络的p阶矩ψ稳定的判别准则,推广了已有的指数稳定性的结论。
关键词: 时滞Cohen-Grossberg神经网络 鞅收敛定理 Lyapunov函数 稳定性
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ψ(t) stability of P moments for Cohen-Grossberg neural networks with time delays
Abstract:INetwork is widely used in information and image processing, pattern recognition, associative memory and combinatorial optimization. They have also attracted wide attention from scholars both at home and abroad. However, in neural networks, the occurrence of time delays may affect the stability of the whole neural network. It is of great theoretical significance to study the stability of stochastic neural networks with time delays. The stability of Cohen Grossberg neural networks with random delays is studied. By using inequality technique, martingale convergence theorem and Lyapunov function, a criterion for p order ψ stability of Cohen Grossberg neural networks with time delay is established, and the existing exponential stability conclusions are extended.
Keywords: Cohen-Grossberg neural network with delay martingale convergence theorem Lyapunov function stability
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时滞Cohen-Grossberg神经网络p阶矩ψ(t)稳定性研究
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