共查询到17条相似文献,搜索用时 62 毫秒
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利用M-矩阵和拓扑学等有关知识,通过构建向量李雅普诺夫函数,研究了一类包含分布时滞和可变时滞的神经网络的平衡点的存在性、唯一性及其全局指数稳定性。在没有假定激励函数有界、可微的情况下,得到了该类神经网络平衡点的存在性、唯一性及其在平衡点全局指数稳定的充分判据。该判据计算简便,且与时间滞后量无关,便于在实践中应用。文中给出了一个算例。 相似文献
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讨论了一类带有时滞的中立型神经网络的稳定性问题。通过构造Lyapunov-Krasovskii泛函,利用矩阵Schur补性质研究了此类中立型时滞神经网络模型的全局渐近稳定性,得出基于矩阵特征值的稳定性的充分判据,并给出基于矩阵特征值的时滞Hopfield神经网络全局渐近稳定性的充分条件;数值仿真检验了结果的有效性。 相似文献
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通过构造适当的Lyapunov函数,利用Halanay不等式和Young不等式,讨论一类具有变时滞的Hopfield型神经网络的全局指数稳定性.在对网络施加两个不同的神经元激励函数的条件下,导出网络全局指数稳定的一个充分条件,得到的充分条件在实际应用中易于验证,且有较小的保守性,因而对网络的应用和设计具有重要意义.最后,一个数值实例进一步验证结果的正确性. 相似文献
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利用M矩阵理论,同构理论以及不等式技巧,研究了一类变时滞神经网络平衡点的存在性和惟一性问题。同时利用M矩阵理论,反证法以及不等式技巧,得到了变时滞神经网络系统惟一的平衡点的全局指数稳定性的充分条件。通过判断由神经网络的权系数、自反馈函数以及激励函数构造的矩阵是否为M矩阵,即可以检验该变时滞神经网络系统的全局指数稳定性。该判据易于用Matlab进行检验,最后给出一个仿真示例进一步证明了判据的有效性。 相似文献
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在近十几年里,已提出了一类与双向联想记忆相联系的神经网络模型,这些模型推广了单层自联想Hebbian相关器为两层异联想模式匹配器,因而,这类网络在模式识别、信号与图像处理等领域中有广阔的应用前景.研究了带离散时滞杂交双向联想记忆神经网络的收敛特性,利用Halanay型不等式获得了网络全局指数稳定性的充分条件,所得结果是与时滞无关的;已证明利用Halanay型不等式获得的结果改进了由Lyapunov方法获得的结果,而且获得的结果容易判定,并且给出了一个数值例子以说明所得结论的正确性. 相似文献
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高娟 《计算技术与自动化》2008,27(4):24-26
研究一类具有时滞的细胞神经网络的稳定性问题,利用Lyapunov—Krasovskii泛函的方法,给出时滞相关的稳定性判据。稳定性判据是以线性矩阵不等式的形式给出,可以很容易得出时滞的上界。在得到时滞相关的稳定性判据的同时也可以得到时滞无关的稳定性判据。数值算例说明其结果的优越性。 相似文献
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Global exponential stability of Hopfield reaction-diffusion neural networks with time-varying delays 总被引:14,自引:0,他引:14
The authors analyze the existence of the equilibrium point and global exponential stability for Hopfield reaction-diffusion neural networks with time-varying delays by means of the topological degree theory and generalized Halanay inequality. Since the diffusion phenomena and time delay could not be ignored in neural networks and electric circuits, the model presented here is close to the actual systems, and the sufficient conditions on global exponential stability established in this paper, which are easily verifiable, have a wider adaptive range. 相似文献
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Employing Brouwer's fixed point theorem, matrix theory, we made a further investigation of a class of neural networks with delays in this paper. A family of sufficient conditions were given for checking global exponential stability. These results have important leading significance in the design and applications of globally stable neural networks with delays. Our results extended and improved some earlier publications. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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In this paper, we investigate the global exponential stability of impulsive high-order Hopfield type neural networks with delays. By establishing the impulsive delay differential inequalities and using the Lyapunov method, two sufficient conditions that guarantee global exponential stability of these networks are given, and the exponential convergence rate is also obtained. A numerical example is given to demonstrate the validity of the results. 相似文献
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Xiaopeng Wei Dongsheng Zhou Qiang Zhang 《Computers & Mathematics with Applications》2009,57(11-12):1938
In this paper, we obtain some sufficient conditions for determining the asymptotic stability of discrete-time non-autonomous delayed Hopfield neural networks by utilizing the Lyapunov functional method. An example is given to show the validity of the results. 相似文献
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In this paper, we discuss impulsive high-order Hopfield type neural networks. Investigating their global asymptotic stability, by using Lyapunov function method, sufficient conditions that guarantee global asymptotic stability of networks are given. These criteria can be used to analyse the dynamics of biological neural systems or to design globally stable artificial neural networks. Two numerical examples are given to illustrate the effectiveness of the proposed method. 相似文献