自适应的显式控制算法*

针对XCP的α参数在网络中流的个数变化明显以及链路带宽相差比较大的网络环境中影响带宽利用率提高的问题,提出一种自适应的XCP改进算法——AXCP。该算法基于平均队列变化率来判断网络的稳定状态,并依此调整参数α。实验表明该算法能有效提高带宽利用率。     

XCP协议的仿真分析

对XCP协议的结构和执行算法进行了详细分析,并对协议做了相应仿真.仿真结果表明,在高带宽时延乘积网络中,XCP协议能更好地保持效率、公平性和稳定性.     

基于XCP协议的拥塞控制研究

对一种全新的拥塞控制协议XCP（eXplicit Control Protocol）进行了研究。在分析传统TCP的拥塞控制机制不足的基础上，对XCP协议的拥塞头格式进行了剖析，给出了基于XCP协议实现拥塞控制的基本原理。最后通过对比实验，验证了XCP协议在拥塞控制性能上比传统的TCP协议更优越。     

基于XCP协议的拥塞控制算法研究

本文首先对TCP协议存在的问题进行了阐述,随着每一流的带宽时延乘积的增长,在不考虑排队方案的条件下,TCP协议变得不稳定和效率低下。然后对XCP协议的结构和执行算法进行详细分析,分析结果表明在高带宽时延乘积网络中,XCP协议能更好的保持效率、公平性和稳定性。     

基于滑模控制的显式控制协议带宽补偿算法

在动态网络中,显式控制协议(XCP)带宽设置不当会使链路利用率大幅下降。针对该问题,提出一种基于滑模控制的XCP带宽补偿算法FC-XCP,通过设计合理的控制器,使输出带宽与链路带宽相匹配、网络系统更稳定。仿真结果表明,与PII-XCP算法相比,FC-XCP能明显提高动态网络的带宽利用率。     

基于最优化流控模型的拥塞控制算法*

在已改进的最优化流控模型和链路价格算法的基础上,提出了一种基于最优化流控模型的拥塞控制算法。NS-2模拟实验结果证明,与类似的显式精确反馈拥塞控制算法XCP相比,新算法有更好的稳定性和相同的带宽利用率。     

显式流量控制协议XCP研究

显式流量控制协议(XCP)能提高网络带宽利用率并改善带宽分配的公平性,本文分析了TCP在带宽时延乘积较大时所存在的性能问题,说明了XCP体系结构和流量控制规则,并对XCP与TCP的带宽时延乘积方面进行了性能比较。     

一种提高XCP协议在大RTT差异环境下的鲁棒性方法

现有TCP协议的拥塞控制机制存在很多不足,XCP(eXplicit Control Protocol)协议采用显式反馈的方式有效地克服了这些缺陷.XCP协议能容忍数据流之间一定的RTT差异,但当这种差异超出一定范围时,XCP协议性能恶化,变得不稳定.通过分析XCP协议路由控制周期与数据流RTT的关系,提出了一种控制周期根据RTT差异程度而自适应调整的方法,消除了系统振荡.仿真数据表明,该方法能有效地提高XCP协议在数据流RTT差异较大的环境下的鲁棒性,同时不给路由器带来过大的计算负担.     

XCP与TCP的拥塞控制算法比较分析

互联网用户数量激增,使得网络的拥塞问题变得越来越严重,拥塞控制是确保Internet鲁棒性的关键因素,因此拥塞控制问题成为目前关于Internet研究的难点问题.分析了传统的TCP拥塞控制算法存在一些不足,并对比分析了一种新的XCP拥塞控制算法.试验结果表明,XCP协议算法具有链路利用效率高、公平性好、可扩展性强、排队时延小的优点,并且路由器的开销也非常小.     

一种增强XCP协议在多瓶颈网络拓扑环境下性能的方法

XCP协议具有很高的效率和良好的最大最小公平性,已成为基于显式反馈的拥塞控制协议的一种标准.进一步研究表明,XCP协议在多瓶颈网络拓扑环境下,当下游存在瓶颈链路时,其性能变差,表现为带宽浪费、收敛时间延长以及公平性迅速恶化.深入分析了XCP协议在单瓶颈拓扑和多瓶颈拓扑环境下的收敛过程,指出了其性能变化的原因,进一步给出了一种结合相对反馈的改进方案,使端系统在收敛阻塞时,能根据瓶颈链路的网络相对负载更新其拥塞窗口.仿真实验表明,改进方案有效地提高了XCP协议在多瓶颈网络拓扑环境下的性能.     

Identification of a Time-Varying Parameter of a Noiseless Sinusoidal Signal

Automation and Remote Control - We consider a new algorithm for estimating the time-varying parameter $$ \omega (t) $$ of a noiseless sinusoidal signal $$ \alpha (t)\sin (\omega (t)+\varphi ) $$ ....     

Evolutionary programming using mutations based on the Levy probability distribution

Studies evolutionary programming with mutations based on the Levy probability distribution. The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter /spl alpha/ in the Levy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Levy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation.     

The conformal alpha shape filtration

Conformal alpha shapes are a new filtration of the Delaunay triangulation of a finite set of points in ℝ^d. In contrast to (ordinary) alpha shapes the new filtration is parameterized by a local scale parameter instead of the global scale parameter in alpha shapes. The local scale parameter conforms to the local geometry and is motivated from applications and previous algorithms in surface reconstruction. We show how conformal alpha shapes can be used for surface reconstruction of non-uniformly sampled surfaces, which is not possible with alpha shapes.     

An iterative Goldstein SAR interferogram filter

This article presents a new modified Goldstein synthetic aperture radar (SAR) interferogram filter algorithm, named the iterative Goldstein filter. The main idea of this approach is to iteratively filter the SAR interferogram, by determining the filtering parameter alpha adaptively with respect to the pseudo-correlation value of the original and/or last filtered interferograms several times. The filter can be stopped automatically by pre-setting the threshold of mean value and the improvement of pseudo-correlation in given filter windows. Experimental results with both a simulated digital elevation model (DEM) interferogram and real SAR deformation interferogram show an improvement in the new algorithm results compared with those using the Goldstein filter, and its enhanced version, the Baran filter. In addition, from a pseudo-correlation map of the iteratively filtered interferogram, some valuable information can also be abstracted based on the signal residues.     

基于参数方差调节萤火虫算法的三维路径规划

为了提高萤火虫算法大范围搜索时的速度和精度,提出了一种参数方差调节萤火虫算法。首先分析基本萤火虫算法,在此基础上提出了参数方差调节萤火虫算法的核心思想：计算种群亮度的方差评估种群的敛散性,根据进程调节参数,进而达到改进萤火虫算法的目的,并给出了算法的实现步骤和流程;然后在四个优化测试函数中将参数方差调节萤火虫算法与基本萤火虫算法、遗传算法、粒子群算法进行比较和分析,发现参数方差调节萤火虫算法在测试中能迅速的找到符合精度要求的解,且成功率是100%,具有较好的稳定性,较之其他算法优势明显;最后通过构建计算能量消耗的目标函数在有实际背景和地理参数的自主式水下潜器三维路径规划的仿真实验中应用参数方差调节萤火虫算法,在三维海底环境中规划出符合要求的路,从而证明了参数方差调节萤火虫算法在三维路径规划中的实用性。     

A stochastic framework for K-SVD with applications on face recognition

In recent years, the sparse representation modeling of signals has received a lot of attention due to its state-of-the-art performance in different computer vision tasks. One important factor to its success is the ability to promote representations that are well adapted to the data. This is achieved by the use of dictionary learning algorithms. The most well known of these algorithms is K-SVD. In this paper, we propose a stochastic framework for K-SVD called \(\alpha\)K-SVD. The \(\alpha\)K-SVD uses a parameter \(\alpha\) to control a compromise between exploring the space of dictionaries and improving a possible solution. The use of this heuristic search strategy was motivated by the fact that K-SVD uses a greedy search algorithm with fast convergence, possibly leading to local minimum. Our approach is evaluated on two public face recognition databases. The results show that our approach yields better results than K-SVD and LC-KSVD (a K-SVD adaptation to classification) when the sparsity level is low.     

A new efficient normal parameter reduction algorithm of soft sets

Kong et al. [Kong, Z., Gao, L., Wang, L., and Li, S., The normal parameter reduction of soft sets and its algorithm, Computers and Mathematics with Applications 56 (12) (2008) 3029-3037] introduced the definition of normal parameter reduction in soft sets and presented a heuristic algorithm of normal parameter reduction. However, the algorithm is hard to understand and involves a great amount of computation. In this paper, firstly, we give some new related definitions and proved theorems of normal parameter reduction. Then we propose a new efficient normal parameter reduction algorithm of soft sets based on the oriented-parameter sum, which can be carried out without parameter important degree and decision partition. The comparison result on a dataset shows that the proposed algorithm involves relatively less computation and is easier to implement and understand as compared with the algorithm of normal parameter reduction proposed by Kong et al.     

基于反馈教学优化算法的混沌系统参数辨识

针对传统智能优化算法对混沌系统参数辨识精度低、速度慢的问题,提出一种基于反馈教学优化算法的混沌系统参数辨识的新方法.该方法以教学优化算法为基础,在教授-学习阶段之后加入反馈阶段,同时将参数辨识问题转化为参数空间上的函数优化问题.分别以三维二次自治广义Lorenz系统、Jerk系统和Sprott-J系统为待辨识模型,对粒子群优化算法、量子粒子群优化算法、教学优化算法及反馈教学优化算法进行了对比实验,反馈教学优化算法辨识误差为零,搜索次数明显减少.仿真结果表明,反馈教学优化算法明显提高了混沌系统参数辨识精度和速度,验证了该算法的可行性和有效性.     

基于核相似性差异最大化的支持向量机参数选择算法

针对支持向量机(SVM)参数选择问题,通过分析SVM近似网络模型及分类原理,提出一种基于核相似性差异最大化的高斯核参数快速选择算法(MSD)。同时,将MSD算法与基于交叉验证的参数搜索算法相结合,构成一种复合SVM参数选择算法(MSD-GS),实现核参数与正则化参数的快速优选。UCI数据的仿真实验表明该算法具有参数选择准确、简便快速、无需数据先验知识等优点,参数选择效果甚至优于遍历式指数网格搜索算法。优选出的参数组合能够使SVM具有较高的泛化性能。     

有效生成饰带群混沌吸引子参数的改进优生遗传算法

本文将蒙特卡罗搜索法与优生遗传算法应用于构造饰带群等价映射模型p112与模型p1a1混沌吸引子,并针对“遗传漂移”现象提出了改进的优生遗传算法．研究表明,在参数空间中引入空间距离的限制,可以由初始种群参数向量搜索出无重复参数向量的子代参数集合．在进化的种群中,也无重复混沌吸引子参数向量,从而避免了原有优生遗传算法在种群中出现的“遗传漂移”现象．新算法实现了种群中的参数无重复地不断更新,利用更新的种群在参数空间上能够持续地搜索出无重复图形结构的混沌吸引子参数向量,解决了原优生遗传算法无法持续有效生成新的混沌吸引子参数向量的问题．