基于云模型改进的粒子群K均值聚类算法

研究粒子群K均值聚类算法问题,针对传统粒子群K均值算法容易陷入局部最优解,出现早熟收敛的缺点,提出一种基于云模型改进的粒子群K均值聚类算法．使用X条件云发生器自适应地调整粒子个体惯性权重的方法．保证惯性权重会逐渐减小而又不失随饥性。根据个体适应度的优劣将粒子群分为三个子群,在每次迭代时都保证仍有一个子群的粒子在进行全局搜索,避免算法陷入局部最优和早熟收敛。在典型数据集上的仿真结果表明,改进算法相比其他聚类算法得到较好的聚类准确率和较快的收敛速度,是一种行之有效的方法。     

一种基于改进KH与KHM聚类的混合数据聚类算法

为解决K-means聚类对初始聚类中心敏感和易陷入局部最优的问题,提出一种基于改进磷虾群算法与K-harmonic means的混合数据聚类算法.提出一种具有莱维飞行和交叉算子的磷虾群算法以改进磷虾群算法易陷入局部极值和搜索效率低的不足,即在每次标准磷虾群位置更新后加入新的位置更新方法进一步搜索以提高种群的搜索能力,同时交替使用莱维飞行与交叉算子对当前群体位置进行贪婪搜索以增强算法的全局搜索能力.20个标准测试函数的实验结果表明,改进算法不易陷入局部最优解,可在较少的迭代次数下有效地搜索到全局最优解的同时保证算法的稳定性.将改进的磷虾群算法与K调和均值聚类融合,即在每次迭代后用最优个体或经过K调和均值迭代一次后的新个体替换最差个体.5个UCI真实数据集的测试结果表明:融合后的聚类算法能够克服K-means对初始聚类中心敏感的不足且具有较强的全局收敛性.     

混沌粒子群与模糊聚类在图像分割中的应用

模糊C均值聚类算法是目前使用最广泛的模糊聚类算法,但是该算法也有其局限性,比如在迭代过程中对初始值非常敏感,极容易陷入局部极小值,以至于得不到最佳聚类结果。将粒子群优化算法应用到模糊C均值聚类算法中,提出一种基于混沌粒子群的模糊C均值聚类算法。它能够利用粒子群算法强大的全局寻优能力避免算法收敛于局部极值,最大程度上达到全局最佳聚类结果。为了避免粒子在迭代过程中停滞,该算法引入了混沌变量,以当前的全局最优位置来产生一个混沌序列,用混沌序列中拥有最优适应值的粒子随机代替当前粒子群中的一个粒子。将基于混沌粒子群的模糊C均值聚类算法应用于图像分割中,实验结果表明该算法能够有效地分割图像,并具有良好的鲁棒性和适应性。     

基于扰动免疫粒子群和K均值的混合聚类算法

针对传统K均值聚类算法对初始化敏感和容易陷入局部最优的缺点,提出了一种基于扰动免疫粒子群和K均值的混合聚类算法。该算法采用K均值将粒子群进行分类,选择平均适应度值最高的聚类域用于产生疫苗,在粒子更新过程中采用疫苗接种机制和免疫选择机制提高粒子的多样性。当个体极值和全局极值连续停滞代数超过所设置的阀值时,算法使用扰动算子改变粒子群的运动方向,提高算法跳出局部极值的能力。当扰动次数达到设置的最大值时,对各个粒子进行K均值操作,提高收敛精度。实验结果表明,该算法具有较高的正确率和较好的稳定性。     

基于改进磷虾群算法的多目标文本聚类方法

提出融合K均值与改进磷虾群算法的多目标文本聚类算法。利用K均值的局部快速寻优和改进磷虾群的全局搜索能力,以K均值聚类解作为改进磷虾群的初始种群,引入遗传交叉和变异改善个体多样性,提升全局搜索能力;通过磷虾种群的诱导运动、觅食运动和随机扩散进行位置更新,引入余弦相似度和欧氏距离的多目标适应度函数评估磷虾位置优劣,搜索全局最优解。通过基准数据集实验确定磷虾群算法的关键参数,进行系统聚类测试,实验结果表明,该算法在聚类指标上表现更佳,聚类准确性更高,收敛速度更快。     

倾斜因子K均值优化数据聚类及故障诊断研究

为提高海量数据挖掘和聚类的效率和精度,以应用于压缩机机械故障智能诊断中.将遗传算法与K均值聚类算法进行互补,提出基于倾斜分类K均值优化数据聚类算法.算法引入倾斜因子,避免较小类数据产生次优解的现象发生.在传统的K均值数据聚类算法基础上,通过倾斜因子排除了少数类局部最优解的干扰,提高遗传算法的收敛速度,也可以避免过早收敛到局部最优解中.仿真实验中以某天然气压气站采集的故障状态下的压缩机振动信号提取的特征量数据作为研究对象,进行数据聚类分析.仿真实验表明,通过改进的数据聚类算法对故障信号关联维特征量进行分类识别,能有效对四类天然气压缩机故障进行诊断分类,准确率能提高18.7％,研究结果在数据优化聚类及在机械故障诊断中的应用中具有良好的指导意义和实践价值.     

融合自控粒子群和免疫进化的入侵数据分类

在对基于异常的入侵检测进行训练时,缺少一个实时有效的训练集,提出了一种融合自控粒子群和免疫进化的入侵数据分类方法,对网络数据进行聚类分析,生成可靠的训练数据。粒子群模糊C均值聚类算法需要提前确定聚类数目,这在网络数据分析处理中是很难把握的,引入自控粒子群的方法根据迭代演算情况自动调节不同聚类数目的粒子群规模,使数据最后聚合在一个数目最优的聚类集中,同时为了克服陷入局部最优的问题,引入免疫进化机制,使部分粒子在当前最优指导下进行合理变异和替换,跳出局部最优解。     

K均值聚类算法在商业银行客户分类中的应用

研究商业银行客户分类优化问题.商业银行客户类别具有多变性,其类别由初始聚类中心来确定,而传统K均值初始聚类中心固定,不能适应客户类别具有多变性,导致商业银行客户分类结果易陷入局部最优,分类准确率极低.为了提高商业银行客户分类的准确率,提出粒子群优化K均值聚类的商业银行客户分类模型.模型将K均值的初始聚类中心作为一个粒子,商业银行客户分类准确率作为粒子群优化的目标函数,通过粒子相互协作获得最优初始聚类中心,聚类中心具有自适应性,使然后采用最优K均值聚类算法对银行客户进行分类.仿真结果表明,优化K均值算法收敛速度快,提高了客户分类准确率,分类结果更加合理,便于对商业银行为客户采取相应经营策略.     

基于GA-IPSO-BSVM算法的新浪微博评论信息分类

针对新浪微博评论信息准确分类问题,本文基于遗传算法(genetic algorithm, GA)、粒子群算法(particle swarm optimization, PSO)和支持向量机(support vector machine, SVM)算法,提出一种改进GA-IPSO-BSVM (genetic algorithm-improved particle swarm optimization-balanced support vector machine)的分类模型,以实现提升新浪微博评论信息分类的准确性和收敛性.首先,为了有效提升算法的收敛速度,并高效节省计算资源,该模型在迭代前期引入GA的淘汰机制,删除大量低速粒子.其次,在迭代中期,为了避免算法陷入局部最优解,改进PSO中粒子关系的拓扑结构,采用K均值聚类(K-means)算法对粒子群进行聚类分区,将各粒子群体在所属社区中进行粒子群迭代,选出各个区域中优秀粒子.再次,在迭代后期,将所有区域优秀粒子组合成优秀粒子群体,并将该群体进行迭代,得出全局最优解.从次,结合GA和IPSO对BSVM进行超参数优化,提升分类准确率.最后,利...     

量子粒子群优化改进的模糊C均值聚类算法

针对模糊C-均值(FCM)聚类算法对初始聚类中心选择敏感,易陷入局部最优的问题,提出一种量子粒子群优化改进的模糊C均值聚类算法。该算法引入的基于新距离标准的量子粒子群(AQPSO)算法不仅可以降低初始点敏感度,较快地收敛到最优解,而且能够提高全局搜索能力。仿真实验证明,该融合算法在摆脱局部最优区域,保证收敛速度同时使得聚类效果较好。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

基于QTE的蒙古文显示机制研究与实现

蒙古语言是中国蒙古族使用的通用语言,由于蒙古文区别于其他文字的书写方式和其自身变形机制等特点,在很多通用的文字处理引擎中都不被支持。在嵌入式产品开发与应用领域中Linux加QTE已经成为流行方式。该文给出了一种在QTE环境上实现基于标准Unicode的蒙古文点阵显示和变形算法, 并自定义了支持蒙古文的QTE组件,扩展了QTE功能,为在Linux加QTE方式的嵌入式体系结构中处理蒙古文提供了一种解决方法。