Existence and global exponential stability of periodic solutions of recurrent cellular neural networks with impulses and delays

By using the continuation theorem of coincidence degree theory and constructing suitable Lyapunov functions, we study the existence, uniqueness and global exponential stability of periodic solutions for recurrent neural networks with impulsive perturbations and delays. Further, by using numerical simulation method, the influences of the impulsive perturbations on the inherent oscillations are investigated.     

Real-time reliability prediction for dynamic systems with both deteriorating and unreliable components

As an important technology for predictive maintenance, failure prognosis has attracted more and more attentions in recent years. Real-time reliability prediction is one effective solution to failure prognosis. Considering a dynamic system that is composed of normal, deteriorating and unreliable components, this paper proposes an integrated approach to perform real-time reliability prediction for such a class of systems. For a deteriorating component, the degradation is modeled by a time-varying fault process which is a linear or approximately linear function of time. The behavior of an unreliable component is described by a random variable which has two possible values corresponding to the operating and malfunction conditions of this component. The whole proposed approach contains three algorithms. A modified interacting multiple model particle filter is adopted to estimate the dynamic system’s state variables and the unmeasurable time-varying fault. An exponential smoothing algorithm named the Holt’s method is used to predict the fault process. In the end, the system’s reliability is predicted in real time by use of the Monte Carlo strategy. The proposed approach can effectively predict the impending failure of a dynamic system, which is verified by computer simulations based on a three-vessel water tank system.     

基于二进制的频繁项集挖掘新算法

频繁项集挖掘是数据挖掘中的一个经典的问题。然而,大部分算法需要扫描数据库多次,算法效率比较低。该文提出了一个效率比较好的挖掘频繁项集的新算法,在这个算法中,所有的事务都是以二进制的形式表示,所以挖掘极大频繁项集的任务就变成了从二进制集中发现频繁模式。而且,这种算法只需要扫描原始数据库一次。最后,利用试验来证明这种算法的效率和优势。     

基于二进制的频繁项集挖掘新算法

频繁项集挖掘是数据挖掘中的一个经典的问题。然而,大部分算法需要扫描数据库多次,算法效率比较低。该文提出了一个效率比较好的挖掘频繁项集的新算法,在这个算法中,所有的事务都是以二进制的形式表示,所以挖掘极大频繁项集的任务就变成了从二进制集中发现频繁模式。而且,这种算法只需要扫描原始数据库一次。最后,利用试验来证明这种算法的效率和优势。     

RAZUMIKHIN-TYPE THEOREMS OF NEUTRAL STOCHASTIC FUNCTIONAL DIFFERENTIAL EQUATIONS

The stability of stochastic functional differential equation with Markovian switching was studied by several authors,but there was almost no work on the stability of the neutral stochastic functional differential equations with Markovian switching.The aim of this article is to close this gap.The authors establish Razumikhin-type theorem of the neutral stochastic functional differential equations with Markovian switching,and those without Markovian switching.     

二值probit回归模型的坍缩变分贝叶斯推断算法

给出了二值probit回归模型的坍缩变分贝叶斯推断算法.此算法比变分贝叶斯推断算法能更逼近对数边缘似然,得到更精确的模型参数后验期望值.如果两个算法得到的分类错误一致,则该算法的迭代次数较变分法明显减少.仿真实验结果验证了所提出算法的有效性.     

基于整数小波变换的指纹信息嵌入方案研究

提出量化指纹编码的思想.将指纹数据转化为二值图像嵌入文件,使攻击者难以发现指纹信息,避免了通过比对编码进行的共谋攻击;设计了整数小波变换嵌入算法,并在实验中得到较好的效果.     

不确定变时滞随机系统的鲁棒均方指数稳定性

研究了不确定变时滞随机系统的鲁棒均方指数稳定性问题, 不确定性是范数有界的. 通过构造Lyapunov泛函, 得到了基于线性矩阵不等式的鲁棒均方指数稳定的充分条件. 最后给出实例加以验证所提出方法的有效性.     

数据库中存取图像

本文介绍了一种把图片等二进制文件在数据库中存取的方法,分析了这种方法和一般方法的不同,给出了此方法的基本原理,并通过实例进行方法的说明及实现。     

二元关系的复合与近似算子的合成

定义了各种类型的经典二元关系和模糊二元关系,讨论了二元关系的合成及其性质.给出了两个近似空间合成的概念,并讨论合成前的近似空间所导出的近似算子与合成后的近似空间所导出的近似算子之间的关系.证明了合成后的近似空间所导出的近似算子恰好是两个近似空间所导出的近似算子的合成.