具无限变时滞的神经网络的稳定性分析

本文研究了具无限变时滞的神经网络的全局指数稳定性，在假设神经元输出输入活化函数有界和满足全局Lipschitz条件下，得到了神经网络具唯一平衡点且该平衡点全局指数稳定的一些充分条件，推广了已有文献中无时滞的相应结果。     

具有时滞的细胞神经网络周期解与稳定性

本文研究了具有时滞的细胞神经网络周期解存在性和平凡解的稳定性问题 .利用 Lyapunov函数法并结合不等式分析技巧 ,我们首先证明了时滞细胞神经网络的解是有界的 ,然后建立了时滞细胞神经网络的周期解的存在准则 ,最后在时滞细胞神经网络有平衡点时 ,给出了神经网络系统的平衡点指数稳定的充分条件 .其结果推广了文 [7,8]的相应结果 .     

一类具分布时滞神经网络的全局指数稳定性

本文讨论了一类具分布时滞神经网络的稳定性.利用广义Dahlquist数和广义Halanay不等式,我们得到了该神经网络平衡点存在、唯一且全局指数稳定的充分条件.此外,我们的方法还估计出了神经网络指数收敛到平衡点的速度.由于我们的方法去除了关于激活函数的有界性、可微性和单调性的常用假设,因此我们的结果是某些现有结果的推广和改进.     

变时滞脉冲BAM神经网络的稳定性(英文)

本文研究了当脉冲不太频繁易受大的脉冲影响下的时滞BAM神经网络具有唯一的指数稳定的平衡点.分析采用一个推广的比较原理和M-矩阵理论,得到一些关于唯一平衡点的收敛性的简单可行的充分条件.所得结果推广和改进了已有文献的结果.     

连续型BAM神经网络的指数稳定性

首先将连续型双向联想记忆神经网络转化成一个特殊的Hopfield网络模型.在此基础上,对连续BAM神经网络的指数稳定性进行了新的分析,证明了神经网络连接权矩阵在给定的约束条件下有唯一平衡点.所做的分析可以用于设计全局指数稳定的神经网络.     

稀疏效应下具常数投放率的食饵-捕食系统的极限环

对一类稀疏效应下具常数投放率的食饵—捕食系统,给出了唯一正平衡点全局稳定的充分条件和存在唯一极限环的充要条件等一些定性性质的判别准则及其生态意义.     

具有时滞的细胞神经网络周期解与稳定性

本文研究了具有时滞的细胞神经网络周期解存在性和平凡解的稳定性问题。利用Ｌｙａｐｕｎｏｖ函数法并结合不等式分析技巧,我们首先证明了时滞细胞网络的解是有界的,然后建立了时滞细胞神经物周期解的存在准则,最后在时滞细胞神经网络有平衡点时,给出了神经网络系统的平衡点指数稳定的充分条件。其结果推广了文「７,８」的相应结果。     

分布时滞反应扩散Hopfield神经网络的全局指数稳定性

利用拓扑度理论和广义Halanay不等式研究了分布时滞反应扩散Hopfield神经网络的平衡点的存在性及全局指数稳定性.给出的判别指数稳定性的代数判据易于验证,具有广泛适用性.     

具有时滞的扩散BAM神经网络的指数稳定性

研究一类具有反应扩散的滞后BAM神经网络平衡点的存在性唯一性和全局指数稳定性.运用拓扑同胚映射,Lyapunov泛函以及多参数方法,得到关于平衡点存在唯一性和全局指数稳定性的充分条件,将相关文献的结果推广到正整数r范数上.     

一类多菌株STD模型的共存性

我们已经研究过一类拥有两种菌株的异性传播的性传染病模型.得到了边界平衡点稳定的充要条件,并确认在边界平衡点的稳定性和正平衡点的存在性之间存在着很强的联系.但是只给出了特殊条件下正平衡点稳定的充要条件,这篇文章将就以前没解决的问题,对这类模型给出完整的分析.     

距离空间中的神经网络插值与逼近

已有的关于插值神经网络的研究大多是在欧氏空间中进行的,但实际应用中的许多问题往往需要用非欧氏尺度进行度量.本文研究一般距离空间中的神经网络插值与逼近问题,即先在距离空间中构造新的插值网络,然后在此基础上构造近似插值网络,最后研究近似插值网络对连续泛函的逼近.     

Exponential synchronization of fuzzy cellular neural networks with mixed delays and general boundary conditions

In this paper, we study exponential synchronization of delayed reaction-diffusion fuzzy cellular neural networks with general boundary conditions. By using Sobolev inequality techniques and constructing suitable Lyapunov functional, some sufficient conditions are given to ensure the exponential synchronization of the drive-response delayed fuzzy cellular neural networks with general boundary conditions. Finally, an example is given to verify the theoretical analysis.     

LMI-based criteria for stability of high-order neural networks with time-varying delay

This paper investigates the stability of a class of high-order neural networks with time-varying delay, which can be considered as an expansion of Hopfield neural networks and is seldom considered in the literature. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, sufficient conditions guaranteeing the global exponential stability of the equilibrium point are presented. Two examples are given to show the effectiveness of the proposed conditions. The obtained results are also shown to be different from and more general than existing ones.     

SCALE-TYPE STABILITY FOR NEURAL NETWORKS WITH UNBOUNDED TIME-VARYING DELAYS

This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.     

神经网络在非线性规划中的应用

本文对蔡少棠[1 ] ,[2 ] 等提出的非线性规划神经网络作了改进 ,使得该神经网络适合于通用VLSI技术实现 .     

Mathematics and Neural Networks –A Glance at some Basic Connections

In the following paper, we present a brief and easily accessible introduction to the theory of neural networks under special emphasis on the rôle of pure and applied mathematics in this interesting field of research. In order to allow a quick and direct approach even for nonspecialists, we only consider three-layer feedforward networks with sigmoidal transfer functions and do not cover general multi-layer, recursive or radial-basis-function networks. Moreover, we focus our attention on density and complexity results while construction problems based on operator techniques are not discussed in detail. Especially, in connection with complexity results, we show that neural networks in general have the power to approximate certain function spaces with a minimal number of free parameters. In other words, under this specific point of view neural networks represent one of the best possible approximation devices available. Besides pointing out this remarkable fact, the main motivation for presenting this paper is to give some more mathematicians an idea of what is going on in the theory of neural networks and, perhaps, to encourage, at least a few of them, to start working in this highly interdisciplinary and promising field, too.     

Activation clustering in neural and social networks

Questions related to the evolution of the structure of networks have received recently a lot of attention in the literature. But what is the state of the network given its structure? For example, there is the question of how the structures of neural networks make them behave? Or, in the case of a network of humans, the question could be related to the states of humans in general, given the structure of the social network. The models based on stochastic processes developed in this article, do not attempt to capture the fine details of social or neural dynamics. Rather they aim to describe the general relationship between the variables describing the network and the aggregate behavior of the network. A number of nontrivial results are obtained using computer simulations. © 2005 Wiley Periodicals, Inc. Complexity 10: 42–50, 2005     

一类BAM神经网络的概周期解的存在性和指数收敛性

利用不动点理论和推广的Halanay时滞微分不等式,研究一类双向联想记忆神经网络的概周期解的存在性和全局指数收敛性,给出其判定的充分性条件.     

一类一般输入输出函数的离散神经元模型的分支

利用经典分支理论研究了一类一般输入输出函数的离散神经元模型的分支问题,得到了该类模型产生倍周期分支和鞍-结点分支的充分条件,推广了甘前特殊的正弦输入输出函数的该类模型的结果.所得的结果为这一类神经网络的应用提供了重要的理论基础.     

Global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays

In this paper, by means of constructing the extended impulsive delayed Halanay inequality and by Lyapunov functional methods, we analyze the global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays. Some new sufficient conditions ensuring exponential stability of the unique equilibrium point of impulsive Hopfield neural networks with time delays are obtained. Those conditions are more feasible than that given in the earlier references to some extent. Some numerical examples are also discussed in this work to illustrate the advantage of the results we obtained.