基于随机游走模型的维基百科语义关系研究

运用随机游走模型提出了一种基于维基百科的语义相关度的计算方法。维基百科中包含了丰富的链接结构,这些链接结构一定程度上能够反应词条之间概念上的相关性,以内容链接和外部链接关系来计算基于维基百科的语义相关度,并在WS-353数据集上进行了实验,取得了较好的准确性。     

自适应收扩系数的双中心协作QPSO算法

针对量子粒子群优化(QPSO)算法迭代后期种群多样性下降、收敛速度慢、易陷入局部最优的缺点,提出一种自适应收缩-扩张系数的双中心协作最子粒子群优化算法。该算法从2个方面进行改进:(1)自适应调节收缩-扩张系数,其目的是帮助粒子跳出局部最优点,提高粒子的全局搜索能力;(2)双重更新全局最优位置,即在每次迭代中,先后分别采用2种不同的方式更新全局最优位置。第1种方式与QPSO算法一致,第2种方式则引入双中心粒子,使其和当前全局最优位置在相应维度上合作,从而达到更新全局最优位置的目的。从固定迭代次数和固定精度角度分析算法性能,仿真结果表明,相比于QPSO算法,该算法在保证复杂度较低的情况下,可提高收敛速度,增强全局和局部搜索能力。     

基于矩阵初等变换的量子可逆逻辑电路双向综合算法

基于矩阵初等变换,提出了量子可逆逻辑电路双向综合算法。该算法依据两数字间的汉明距离,通过交换矩阵行号或矩阵元素对量子可逆逻辑电路的矩阵进行初等行变换。在变换的过程中,利用邻接矩阵的电路转化规则,生成任意给定置换的量子可逆逻辑电路。与其它同类算法相比,由于不需要穷尽搜索,该算法的时空复杂度有大幅降低;又由于采用任意n量子扩展通用Toffoli门,该算法可综合任一置换(奇或偶置换)的量子可逆逻辑电路,并且电路中门的数量有所减少。     

基于多权重相似度的随机漫步推荐算法

在传统个性化推荐算法的基础上,提出了一种基于多权重相似度的随机漫步推荐算法。为了解决传统协同过滤算法中忽略了社交网络、热门项目以及共同评分项目之间影响等问题,通过引入万有引力公式计算社交网络中的用户相似度,并对传统协同过滤算法中的相似度进行改进,采用权重因子结合这两者相似度,最后开拓性地结合随机漫步算法进行商品推荐。实验结果表明,提出的算法具有比其他推荐算法更好的推荐性能。     

Detection of delamination in laminated composites with limited measurements combining PCA and dynamic QPSO

This paper presents an output only damage diagnostic algorithm based on frequency response functions and the principal components for health monitoring of laminated composite structures. The principal components evaluated from frequency response data, are employed as dynamical invariants to handle the effects of operational/environmental variability on the dynamic response of the structure. Finite element models of a laminated composite beam and plate are used to generate vibration data for healthy and damaged structures. Three numerical examples include a laminated composite beam, cantilever plate made of carbon–epoxy and a laminated composite simply supported plate. Varied levels of delamination of laminated composite plies and matrix cracking at varied locations in the plies are simulated at different spatial locations of the structure. Numerical investigations have been carried out to identify the spatial location of damage using the proposed principal component analysis (PCA) based algorithm. In order to limit the number of sensors on the structure, an optimal sensor placement algorithm based on PCA is employed in the present work and the effectiveness of the proposed algorithm with a limited number of sensors is also investigated. Finally, the inverse problem associated with the detection of delamination and matrix cracking is formulated as an optimization problem and is solved using the newly developed dynamic quantum particle swarm optimization (DQPSO) algorithm. Studies carried out and presented in this paper clearly indicate that the proposed SHM scheme can robustly identify the instant of damage, spatial location, the extent of delamination and matrix cracking even with limited sensor measurements and also with noisy data.     

基于随机游走的大规模图中节点对采样算法

社会网络中的节点对采样可用于大规模社会网络的好友预测和用户兴趣识别.当整个网络的拓扑结构不完全或者随机选择用户的代价很高时,传统的均匀顶点采样方法的性能迅速下降.为此,提出了一种基于随机游走的大规模图中节点对采样算法.首先对社会网络的节点对采样进行了系统分析,对不同跳数下的节点对进行了定义;然后将社会网络转换成等价的网络图.新图中的顶点是原图中的边,新图中边的两个顶点是原图中含有相同顶点的两条边.最后,在新图上应用随机游走模型对节点对进行采样.实验结果表明,提出的方法统计误差小、执行效率高,性能明显优于均匀节点采样的相关算法.     

分形结构中粒子扩散过程随机模拟

为分析孔隙分形维数和分形结构形式对粒子扩散特性的影响,通过生成分形结构模拟粒子在分形结构中的随机行走,探索分形介质中粒子扩散的运动规律.数值模拟结果显示,粒子在分形结构中的扩散行为受到起点位置、主通道和次通道形状及分形维数等因素的影响.     

一种基于改进的量子神经网络的语音降噪方法

利用一种改进的量子神经网络（IPSO-QNN）在时域上对含噪的语音信号进行降噪处理,文中重点改进了QNN所涉及到的学习算法。针对粒子群算法本身存在早熟的不足,提出了一种改进的粒子群优化算法（IPSO）。该算法通过对早熟粒子的速度和位置叠加随机数据,使其离开局部最优,从而使算法具有更强的寻优能力。利用IPSO对量子神经网络的参数进行训练和学习,建立了比较高效的基于改进的量子神经网的语音信号滤波器,并通过Matlab软件建立实验平台,实验结果表明,新算法充分利用了量子神经计算的快速性以及粒子群算法的全局寻优能力,从而使该语音信号滤波器,具有良好的降噪性能。     

一种量子神经网络模型及改进学习算法

提出一种量子BP网络模型及改进学习算法,该BP网络模型首先基于量子学中一位相移门和两位受控非门的通用性,构造出一种量子神经元,然后由该量子神经元构造隐含层,采用梯度下降法进行学习。输出层采用传统神经元构造,采用基于改进的带动量自适应学习率梯度下降法学习。在UCI两个数据集上采用该模型及算法,实验结果表明该方法比传统的BP网络具有较好的收敛速度和正确率。     

Quantum Algorithms for Weighing Matrices and Quadratic Residues

Abstract. In this article we investigate how we can employ the structure of combinatorial objects like Hadamard matrices and weighing matrices to devise new quantum algorithms. We show how the properties of a weighing matrix can be used to construct a problem for which the quantum query complexity is significantly lower than the classical one. It is pointed out that this scheme captures both Bernstein and Vazirani's inner-product protocol, as well as Grover's search algorithm. In the second part of the article we consider Paley's construction of Hadamard matrices, which relies on the properties of quadratic characters over finite fields. We design a query problem that uses the Legendre symbol χ (which indicates if an element of a finite field F _q is a quadratic residue or not). It is shown how for a shifted Legendre function f _s (i)=χ(i+s) , the unknown s ∈ F _q can be obtained exactly with only two quantum calls to f _s . This is in sharp contrast with the observation that any classical, probabilistic procedure requires more than log q + log ((1-ɛ )/2) queries to solve the same problem.