共查询到16条相似文献,搜索用时 62 毫秒
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具时滞脉冲细胞神经网络的全局指数稳定性 总被引:2,自引:0,他引:2
研究了一类新的具有脉冲的时滞细胞神经网络系统模型,引入了一类新的脉冲条件,在不假设激励函数的有界性、单调性和光滑性的条件下,得到了系统平衡点的存在性、唯一性及全局指数稳定性的一些新的充分条件,并得到了指数收敛速率. 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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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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研究等式约束下二次规划问题最优解神经网络模型的稳定性,提出一种变时滞Lagrange神经网络求解方法.利用线性矩阵不等式(LMI)技术,得到两个变时滞神经网络模型全局指数稳定的条件.分析表明,此稳定判据能够适应慢变时滞和快变时滞两种情况,具有适用范围宽、保守性小且易于验证等特点.数值仿真结果验证了所提方法的有效性. 相似文献
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针对一类不确定大规模系统,研究其全局稳定的分散自适应神经网络反推跟踪控制问题.在假设不匹配的未知关联项满足部分已知的非线性Lipschitz条件下,采用神经网络作为前馈补偿器,逼近参考信号作为输入的未知关联函数;设计者可根据参考信号的界预先确定神经网络逼近域,同时保证了闭环系统的全局稳定性.仿真实例验证了控制算法的有效性. 相似文献
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Some sufficient conditions for the global exponential stability of cellular neural networks with variable coefficients and
time-varying delays are obtained by a method based on a delayed differential inequality. The method, which does not make use
of Lyapunov functionals, is simple and effective for the stability analysis of cellular neural networks with variable coefficients
and time-varying delays. Some previous results in the literature are shown to be special cases of our results.
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Ailong WuAuthor Vitae Zhigang ZengAuthor VitaeWenwen ShenAuthor Vitae 《Neurocomputing》2011,74(5):831-837
In this paper, global exponential stability in Lagrange sense for periodic neural networks with various activation functions is further studied. By constructing appropriate Lyapunov-like functions, we provide easily verifiable criteria for the boundedness and global exponential attractivity of periodic neural networks. These theoretical analysis can narrow the search field of optimization computation, associative memories, chaos control and provide convenience for applications. 相似文献
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In this article, the global exponential stability problem of Cohen--Grossberg neural networks with both discrete-time delays and distributed delays is investigated. The existence and global stability for the unique equilibrium of the Cohen--Grossberg neural networks with distributed delays are achieved by using some new Lyapunov functionals, M-matrix theory and some analytic techniques, and some less restrictive conditions are obtained. An example is also worked out to validate the advantages of our results. 相似文献
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Quanjun WuAuthor Vitae Jin ZhouAuthor Vitae Lan XiangAuthor Vitae 《Neurocomputing》2011,74(17):3204-3211
The present paper formulates and studies a model of recurrent neural networks with time-varying delays in the presence of impulsive connectivity among the neurons. This model can well describe practical architectures of more realistic neural networks. Some novel yet generic criteria for global exponential stability of such neural networks are derived by establishing an extended Halanay differential inequality on impulsive delayed dynamical systems. The distinctive feature of this work is to address exponential stability issues without a priori stability assumption for the corresponding delayed neural networks without impulses. It is shown that the impulses in neuronal connectivity play an important role in inducing global exponential stability of recurrent delayed neural networks even if it may be unstable or chaotic itself. Furthermore, example and simulation are given to illustrate the practical nature of the novel results. 相似文献
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In this paper, almost sure exponential stability of neutral delayed cellular neural networks which are in the noised environment is studied by decomposing the state space to sub-regions in view of the saturation linearity of output functions of neurons of the cellular neural networks. Some algebraic criteria are obtained and easily verified. Some examples are given to illustrate the correctness of the results obtained. 相似文献
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In this paper, almost sure exponential stability of neutral delayed cellular neural networks which are in the noised environment is studied by decomposing the state space to sub-regions in view of the saturation linearity of output functions of neurons of the cellular neural networks. Some algebraic criteria are obtained and easily verified. Some examples are given to illustrate the correctness of the results obtained. 相似文献