人工神经网络求解TSP问题新方法

本文在分析Hopfield/Tank方法的基础上提出一种新的人工神经网络方法,采用优化约束条件的能量函数,具有收敛速度快、不易陷入无效解、易获得亚优解等优点。     

一种微博用户影响力的计算方法

针对微博中用户影响力分析这个问题,提出用户影响力的计算方法。该方法首先提出用户自身影响力以及用户被影响力的概念,并根据用户自身特征与用户粉丝情况得出其计算公式,从而可以综合考虑用户在微博中的所有信息,计算出用户影响力。实验结果表明,这种计算方法能比较好地反映用户在其粉丝中的影响力。     

网络系统可靠性评估的一种算法

本文给出一般网络系统可靠性评估的一种有效算法,首先系统化为等价的网络图,利用开关网络的理论定义系统成功函数,给出求网络的输入出节点的所有最小路集的方法;其次,定义排它算子,对网络进行有效、简便不交和处理,并导其出可靠性表达式,最后给出范例以说明算法执行过程。     

A NEW METHOD FOR REPRESENTING THE CIRCLE BY NURBS

In this paper, a new algorithm for representing conics precisely is presented using NURBS ofdegree two. The algorithm requires only four control vertices, which is the minimum number of controlvertices compared with the other algorithms using the square control vertice configuration, to representa full circle using the full circumscribing square based representation. This paper also derives a new con-dition for NURBS to precisely represent conics and shows the necessary and sufficient condition of rep-resentation of a circular arc.     

一种基于模糊理论的Web服务信任评估方法

信任评估是可信软件中的核心问题.针对Web服务信任的不确定性和主观性特点,引进模糊理论,给出了一种Web服务信任的综合评估方法,同时,以例子说明了这种方法的应用.最后,描述了评估模型的实现.     

一种计算机辅助评估儿童急性淋巴细胞白血病预后新方法

介绍一种计算机辅助评估儿童急性淋巴细胞白血病预后的新方法,并对该方法的原理及主要步骤进行了详细阐述和分析.通过检验,所得结果与医务人员的结论一致,证明了该方法的可行性.     

分布式多传感器决策融合的新方法——双端检验法

利用传统的Ｂａｙｅｓｉａｎ决策理论和假设检验方法处理分布决策融合问题有一个重要的缺陷就是缺乏柔性，无法将不确定与不知道区分开来；Ｄｅｍｐｓｔｅｒ－Ｓｈａｆｅｒ证据理论虽然可以弥补这一缺陷，但作为其数学基础的公理化定义的严密性值得怀疑。因此需要有更完善、更可靠和更有效的统计决策及证据组合方法。在这方面Ｔｈｏｍｏｐｏｕｌｏｓ已提出了一种广义证据处理方法（ＧＥＰ），它由Ｂａｙｅｓｉａｎ理论与Ｄ－Ｓ理论相     

基于粗糙集及RBF网络的英文字母识别

将粗糙集理论与神经网络相结合,针对7×5分辨率的大写英文字母,构建了基于RBF网络的字母识别系统,给出了该识别系统的核心算法与核心结构.该系统利用粗糙集中最小决策算法对识别矩阵进行属性约简,减少了大量的计算和数据库存储量,同时提高了系统识别速度和识别率.通过计算机模拟实验,将该识别系统的识别率与标准BP网络算法及改进BP网络算法相比较,证实了该系统的优越性,在有约1/7的像素点受到随机干扰的情况下,该系统识别率仍可达到88%以上.     

基于知识的快速角点提取

本文提出一种基于查表技术知识的基角点提取方法．就一定的考察点数目来说，该方法由于采用了人的知识，其检测效果是很好的．由于采用了查表技术，该方法的速度非常快，并且易于硬件实现．当用硬件实现该方法，对于５１２×５１２的真实图像，角．点提取能以电视速率进行．本文详细讨论了查找表的构造方法和知识的采集方法．实验表明该方法工作得很好．     

A NEW HYBRID ALGORITHM FOR MULTI-OBJECTIVE DISTRIBUTION FEEDER RECONFIGURATION

This article proposes an efficient hybrid algorithm for multi-objective distribution feeder reconfiguration. The hybrid algorithm is based on the combination of discrete particle swarm optimization (DPSO), ant colony optimization (ACO), and fuzzy multi-objective approach called DPSO-ACO-F. The objective functions are to reduce real power losses, deviation of nodes voltage, the number of switching operations, and the balancing of the loads on the feeders. Since the objectives are not the same, it is not easy to solve the problem by traditional approaches that optimize a single objective. In the proposed algorithm, the objective functions are first modeled with fuzzy sets to calculate their imprecise nature and then the hybrid evolutionary algorithm is applied to determine the optimal solution. The feasibility of the proposed optimization algorithm is demonstrated and compared with the solutions obtained by other approaches over different distribution test systems.     

基于混合统计模型的中文基本名词短语识别

采用一种基于混合统计模型的方法来实现中文基本名词短语识别。首先简要分析目前的研究现状,明确中文Base NP识别的任务,然后采用以基于转换的标注和条件随机域模型为底层,支持向量机模型为高层的混合统计模型来进行中文BaseNP的识别。在ACE2005中文语料上的实验表明,F值比使用单一模型提高了1.37%,达到了88.67%,能提高中文基本名词短语的识别性能。     

一种基于负载均衡和能量感知的移动自组网路由算法

在移动自组网中，减少移动节点电池能量消耗，延长网络总的使用时间，已经成为路由协议性能评价的重要方面。提出了一种新的路由选择度量，它综合考虑了节点负载及当前的剩余能量，试图通过路由机制均衡网络流量、保护网络中的低能量节点。结合此度量方式提出一种路由选择算法LBEAR（Load—Balanced ＆ Energy—Aware Routing），仿真结果表明，该算法能够使节点能耗与负载的分布更为均匀，相比以前相关的路由算法有效提高了吞吐量。     

适用于分布式虚拟环境的MULTICAST通信协议设计与分析

本文主要讨论了分布式虚拟环境（ＤＶＥ）的通信特点及其对网络通信协议的要求,提出了一个基于Ｍｕｌｔｉｃａｓｔ分组（ＭＧ）的通信思想,据此设计实现了一个适用于ＤＶＥ系统的通信协议（基于发送方的分组Ｍｕｌｔｉｃａｓｔ通信协议－－ＳＩＧＭＰ）。最后论文对Ｍｕｌｔｉｃａｓｔ通信和ＳＩＧＭＰ协议进行了详细地性能分析和讨论,推导出了对Ｍｕｌｔｉｃａｓｔ通信进行性能分析的一般方法。编程实现的ＳＩＧＭＰ协议可在Ｉｎ     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

A NEW DESIGN APPROACH TO DELAY‐DEPENDENT ROBUST H∞ CONTROL FOR UNCERTAIN TIME‐DELAY SYSTEMS

A new design approach to delay‐dependent robust stabilization and robust H∞ control for a class of uncertain time‐delay systems is provided in this paper. The sufficient conditions for delay‐dependent robust stabilization and robust H∞ control are derived based on a new state transformation and given in terms of linear matrix inequalities (LMI). Numerical examples are presented to show that the proposed results can be less conservative and can be used to deal with not only small but also large delay systems.