具漏泄时滞的细胞神经网络多周期性分析

针对一类具有漏泄时滞细胞神经网络模型,首先给出该类网络的周期环在饱和区局部指数收敛的充分条件.研究表明,一个n维网络可以有2n个周期环存在于饱和区,并且这些周期环是局部指数收敛的.然后,研究了该时滞细胞神经网络指数周期的一个特殊情形--指数稳定.数值例子和仿真结果验证了所得结果的有效性.     

具漏泄时滞的细胞神经网络多周期性分析

针对一类具有漏泄时滞细胞神经网络模型,首先给出该类网络的周期环在饱和区局部指数收敛的充分条件.研究表明,一个n维网络可以有2"个周期环存在于饱和区,并且这些周期环是局部指数收敛的.然后,研究了该时滞细胞神经网络指数周期的一个特殊情形---指数稳定.数值例子和仿真结果验证了所得结果的有效性.

     

一类高阶Cohen-Grossberg神经网络的多周期性分析

利用状态空间分解方法,探讨一类具有特殊激励函数的高阶Ｃｏｈｅｎ-Ｇｒｏｓｓｂｅｒｇ神经网络的多周期性问题．该类神经网络的激励函数包括带有饱和区的非递减函数以及一般的细胞神经网络激励函数等．给出了保证此类网络的周期环在饱和区内局部指数收敛的充分条件．所得结果表明,一个狀维网络可以有２ⁿ个局部指数收敛的周期环存在于饱和区．最后以一个数值例子说明了所得结果的有效性．     

一类高阶Ｃｏｈｅｎ-Ｇｒｏｓｓｂｅｒｇ神经网络的多周期性分析

利用状态空间分解方法,探讨一类具有特殊激励函数的高阶Ｃｏｈｅｎ-Ｇｒｏｓｓｂｅｒｇ神经网络的多周期性问题．该类神经网络的激励函数包括带有饱和区的非递减函数以及一般的细胞神经网络激励函数等．给出了保证此类网络的周期环在饱和区内局部指数收敛的充分条件．所得结果表明,一个狀维网络可以有２ⁿ个局部指数收敛的周期环存在于饱和区．最后以一个数值例子说明了所得结果的有效性．

     

BP神经网络的饱和分析及改进

为了改善BP神经网络易形成局部最小,收敛速度慢的缺点,从分析三个因子学习因子、惯性因子和形状因子对BP算法性能影响出发,提出了离线调整学习因子和惯性因子,在线调整形状因子的联合优化方法.这种方法使网络在训练时,不仅神经元的连接权在不断调整,而且其自身的输入输出关系也在变动,从而使网络脱离饱和区,提高了收敛速度.最后以最典型应用函数逼近和XOR分类为例进行验证,仿真结果显示,联合优化方法不仅提高了网络训练速度,还提高了收敛精度,而且比一般的改进方法效果更好,具有一定的实用价值.     

基于遗传模拟退火算法的无线传感器网路由协议

在无线传感器网络中(WSNs)中,由于节点能量有限,为了延长整个网络的生存周期,提出一种基于遗传模拟退火算法的无线传感器网络路由协议.利用模拟退火(SA)算法具有较强的局部搜索能力并能以稳定的速度收敛,克服遗传算法(GA)局部搜索能力差并容易早熟收敛等缺点.该路由协议在簇头节点选举时充分考虑了节点的剩余能量,并根据网络中数据转发能量耗损和延迟时间建立个体适应度函数,采用遗传模拟退火算法找到簇头节点到基站的最优路径.仿真结果表明:与其他协议比较,该方法不仅可以均衡各个节点的剩余能量,还可以有效延长整个网络生存周期和提高网络的数据传输能力.     

具有混沌激励函数的BP网络算法

该文讨论了BP网络学习过程中的假饱和现象，并给出了一种改进的算法，有效地解决了假饱和的问题。仿真结果表明，该方法不但可以提高网络学习的快速性，而且具有一定的避免权值落入局部极小点的能力，从而提高了网络的收敛精度，同时，该算法还能提高网络的泛化能力。     

延迟离散Hopfield网络的动态特征分析

神经网络的稳定性被认为是神经网络各种应用的基础．主要利用网络的状态转移方程和能量函数来研究带有延迟项的离散Hopfield神经网络动力学行为．给出了延迟离散Hopfield神经网络收敛于周期小于等于2的极限环的一些充分条件．给出了延迟网络收敛于周期为2和4的特殊极限环的一些充分条件．同时，得到了网络不存在任何稳定点的一些必要条件．所获结果不仅推广了一些已有的结论，而且为网络的应用提供了一定的理论基础．     

基于自适应周期的流言机制快速构建自组Overlay拓扑

分析了overlay拓扑管理中流言机制的一般过程,发现固定周期的流言报文中存在的数据交换盲目性的弊端.为此,引入动态的自适应用期来代替固定周期,使得局部拓扑稳定的节点较少发出流言报文,而局部拓扑动荡的节点较多发出流言报文.这种方法提高了数据交换效率,节省了网络资源,允许在局部加快数据交换,从而提高拓扑的整体收敛速度.通过仿真实例对该方法的有效性进行了验证,并证实该方法在动态网络环境中尤其适用.     

基于区域划分的WSN非均匀多跳分簇路由算法

延长网络生存周期是WSN的核心问题之一.为均衡网络能耗,有效延长网络生存周期,提出一种保证区域能耗均衡的非均匀多跳分簇路由算法.通过对监测区域的等间距环形划分和等夹角扇形划分,得到同环簇大小相等、不同环簇大小由外到里依次递减的非均匀分簇方案,保证网络能耗效率最优.在簇头选取阶段,通过与距离相关的通信代价评价函数在每个子区域选择最合适的节点作为簇头,减少网络局部能耗.仿真结果表明了所提出算法的有效性.     

Multiperiodicity and exponential attractivity evoked by periodic external inputs in delayed cellular neural networks

We show that an n-neuron cellular neural network with time-varying delay can have 2(n) periodic orbits located in saturation regions and these periodic orbits are locally exponentially attractive. In addition, we give some conditions for ascertaining periodic orbits to be locally or globally exponentially attractive and allow them to locate in any designated region. As a special case of exponential periodicity, exponential stability of delayed cellular neural networks is also characterized. These conditions improve and extend the existing results in the literature. To illustrate and compare the results, simulation results are discussed in three numerical examples.     

Multistability of periodic delayed recurrent neural network with memristors

This paper discusses the recurrent neural network (RNN) with memristors as connection weights. Memristor is a nonlinear resistor. Memristance varies periodically with time when the sinusoidal voltage source is applied. According to this property of memristor, it shows that coefficients of RNN with memristors are periodic functions with respect to time t. By dividing the state space and using contraction mapping theorem, one sufficient condition is obtained for multiperiodicity. And the periodic orbits located in saturation regions are locally exponentially stable limit cycles. At last, one example is given for verifying the validity of our result.     

Multistability analysis for a general class of delayed Cohen–Grossberg neural networks

In this paper, by discussing parameter conditions based on properties of activation functions, we decompose state space into positively invariant sets and establish sufficient conditions for the existence of locally stable equilibria for delayed Cohen–Grossberg neural networks (CGNNs) through Cauchy convergence principle. Some new criteria are derived for ensuring equilibria (periodic orbits) to be locally or globally exponentially stable in any designated region. Finally, our results are demonstrated by four numerical simulations.     

Attractor switching by neural control of chaotic neurodynamics

Chaotic attractors of discrete-time neural networks include infinitely many unstable periodic orbits, which can be stabilized by small parameter changes in a feedback control. Here we explore the control of unstable periodic orbits in a chaotic neural network with only two neurons. Analytically, a local control algorithm is derived on the basis of least squares minimization of the future deviations between actual system states and the desired orbit. This delayed control allows a consistent neural implementation, i.e. the same types of neurons are used for chaotic and controlling modules. The control signal is realized with one layer of neurons, allowing selective switching between different stabilized periodic orbits. For chaotic modules with noise, random switching between different periodic orbits is observed.     

联想记忆神经网络局部指数稳定的充要条件及特征函数

讨论非线性连续联想记忆神经网络平衡点局部指数稳定的判定条件及平衡点指数 吸引域的估计,得到了平衡点局部指数稳定的充要条件,并引入一个特征函数,可以判定平衡 点的邻域是否为指数吸引域.文中给出一族范数下(所有单调范数)网络局部或全局指数稳定 的判定条件,推广了已知文献在特定范数下所得到的结论.     

含时延的联想记忆神经网络的指数吸引域和指数收敛速度

采用不等式技巧和非负矩阵性质, 给出了含时延的联想记忆神经网络平衡点的指数吸引域和指数收敛速度估计以及指数稳定的一些判断条件.     

Global exponential stability of neural networks with globallyLipschitz continuous activations and its application to linearvariational inequality problem

This paper investigates the existence, uniqueness, and global exponential stability (GES) of the equilibrium point for a large class of neural networks with globally Lipschitz continuous activations including the widely used sigmoidal activations and the piecewise linear activations. The provided sufficient condition for GES is mild and some conditions easily examined in practice are also presented. The GES of neural networks in the case of locally Lipschitz continuous activations is also obtained under an appropriate condition. The analysis results given in the paper extend substantially the existing relevant stability results in the literature, and therefore expand significantly the application range of neural networks in solving optimization problems. As a demonstration, we apply the obtained analysis results to the design of a recurrent neural network (RNN) for solving the linear variational inequality problem (VIP) defined on any nonempty and closed box set, which includes the box constrained quadratic programming and the linear complementarity problem as the special cases. It can be inferred that the linear VIP has a unique solution for the class of Lyapunov diagonally stable matrices, and that the synthesized RNN is globally exponentially convergent to the unique solution. Some illustrative simulation examples are also given.     

Exponential Lagrange stability for impulses in discrete-time delayed recurrent neural networks

This paper focuses on the problem of exponential stability in the sense of Lagrange for impulses in discrete-time delayed recurrent neural networks. By establishing a delayed impulsive discrete inequality and a novel difference inequality, combining with inequality techniques, some novel sufficient conditions are obtained to ensure exponential Lagrange stability for impulses in discrete-time delayed recurrent neural networks. Meanwhile, exponentially convergent scope of neural network is given. Finally, several numerical simulations are given to demonstrate the effectiveness of our results.