Blind Signal Separation Based on Quantum Genetic Algorithm

Blind Signal Separation （BSS） is one of the typical problems in array processing and data analysis. In this paper, a novel method of blind signal separation based on Quantum Genetic Algorithm （QGA） is proposed. QGA is a more effective way in comparison with Conventional Genetic Algorithm （CGA）.     

量子算法模拟系统研究现状

The Quantum Computer has immense power,exceeds the capabilities of a classical computer,but the hardware of such machine is still in research. If we want to develop quantum algorithms,wemust simulate them on classical computer. In this paper ,we first introduce the principle and model usedin quantum computing,and compare the simulators in tile world. At last ,based on the problems in simu-lation,we give a new architecture of quantum algorithm simulator.     

并行遗传算法研究进展

Parallel Genetic Algorithm is an important branch of Evolutionary Computing and more and more AI specialists pay attention to it because of its simplified topologies and robust search a-bility. In this paper, we discussed theoretic and application achievement of Parallel Genetic Algorithm ,analyzed their advantage and disadvantage ,and point out its future research direction.     

A novel model on dynamic resource allocation in optical networks

A novel model on dynamic resource allocation in the WDM optical networks is proposed, basing on the integrated considerations of the impacts of transmission impairments and service classification on dynamic resource allocation in the optical layer. In this model, the priorities of optical connection requests are mapped into different thresholds of transmission impairments, and a uniform method which is adopted to evaluate the virtual wavelength path (VWP) candidates is defined. The Advanced Preferred Wavelength Sets Algorithm (A-PWS) and the heuristic Dynamic Min-Cost & Optical Virtual Wavelength Path Algorithm (DMC-OVWP) are presented addressing the routing and wavelength assignment (RWA) problem based on dynamic traffic and multi priorities in wavelength-routed optical networks. For a received optical connection request, DMC-OVWP is employed to calculate a list of the VWP candidates, and an appropriate VWP which matches the request's priority is picked up to establish the lightpath by analyzing the tra     

Some Issues in Quantum Information Theory

Quantum information theory is a new interdisciplinary research field related to quantum mechanics, computer science, information theory, and applied mathematics. It provides completely new paradigms to do information processing tasks by employing the principles of quantum mechanics. In this review, we first survey some of the significant advances in quantum information theory in the last twenty years. We then focus mainly on two special subjects： discrimination of quantum objects and transformations between entanglements. More specifically, we first discuss discrimination of quantum states and quantum apparatus in both global and local settings. Secondly, we present systematical characterizations and equivalence relations of several interesting entanglement transformation phenomena, namely entanglement catalysis, multiple-copy entanglement transformation, and partial entanglement recovery.     

Editorial

Quantum technology is recognized as one of the most promising directions for our future technologies. By exploiting the unique features of quantum effects, emerging quantum technology is rapidly progressing around the world including quantum computation, quantum communication, quantum metrology and quantum simulation. The development of quantum control plays a grounding role in enabling the preservation and manipulation of quantum states that are used in various quantum technologies. This special issue will focus on new development in relevant topics of estimation and control methods in quantum systems, and provide a forum for idea exchange in this dynamic research area. Particularly, Ian R. Petersen, a key figure in the development of robust and quantum control theory, just turned 60 in the past year. We propose to celebrate this occasion with this special issue on quantum control, one of his main research focuses in the last fifteen years....     

Editorial

This special issue is to honor professor Tzyh-Jong Tarn for his 80 birthday. Prof. Tarn is a world outstanding expert in Systems and Control Theory, Robotics and Quantum Control, etc. He received the D.Sc. degree in control system engineering from Washington University at St. Louis, Missouri, U.S.A. He is at present a Professor in the Department of Electrical Engineering and the Director of the Center for Robotics and Automation at Washington University. Dr. Tarn served as the President of the IEEE Robotics and Automation Society, the Director of IEEE Division X (Systems and Control), and a member of the IEEE Board of Directors. The Japan Foundation for the Promotion of Advanced Automation Technology presented him with the Best Research Article Award in March 1994. He also is a recipient of the Best Paper Award at the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Prof. Tarn has also made significant contribution to enhance the academic exchange between Chinese scholars and world systems science and automation society. This special issues contains 10 papers covering 1) Robot Control; 2) Quantum Control; 3) System Identification; 4) Advanced linear/Nonlinear Control Technique; and 5) Multi-agent Systems. These fields are closely related to Prof. Tarn''s research interests. The papers are arranged according to their categories....     

Modeling and multiobjective optimization of traction performance for autonomous wheeled mobile robot in rough terrain

Application of terrain-vehicle mechanics for determination and prediction of mobility performance of autonomous wheeled mobile robot(AWMR) in rough terrain is a new research area currently receiving much attention for both terrestrial and planetary missions due to its significant role in design,evaluation,optimization,and motion control of AWMRs.In this paper,decoupled closed form terramechanics considering important wheel-terrain parameters is applied to model and predict traction.Numerical analysis of traction performance in terms of drawbar pull,tractive efficiency,and driving torque is carried out for wheels of different radii,widths,and lug heights,under different wheel slips.Effects of normal forces on wheels are analyzed.Results presented in figures are discussed and used to draw some conclusions.Furthermore,a multiobjective optimization(MOO) method for achieving optimal mobility is presented.The MOO problem is formulated based on five independent variables including wheel radius r,width b,lug height h,wheel slip s,and wheel rotation angle θ with three objectives to maximize drawbar pull and tractive efficiency while minimizing the dynamic traction ratio.Genetic algorithm in MATLAB is used to obtain optimized wheel design and traction control parameters such as drawbar pull,tractive efficiency,and dynamic traction ratio required for good mobility performance.Comparison of MOO results with experimental results shows a good agreement.A method to apply the MOO results for online traction and mobility prediction and control is discussed.     

WNN-Based Network Security Situation Quantitative Prediction Method and Its Optimization

The accurate and real-time prediction of network security situation is the premise and basis of preventing intrusions and attacks in a large-scale network. In order to predict the security situation more accurately, a quantitative prediction method of network security situation based on Wavelet Neural Network with Genetic Algorithm （GAWNN） is proposed. After analyzing the past and the current network security situation in detail, we build a network security situation prediction model based on wavelet neural network that is optimized by the improved genetic algorithm and then adopt GAWNN to predict the non-linear time series of network security situation. Simulation experiments prove that the proposed method has advantages over Wavelet Neural Network （WNN） method and Back Propagation Neural Network （BPNN） method with the same architecture in convergence speed, functional approximation and prediction accuracy. What is more, system security tendency and laws by which security analyzers and administrators can adjust security policies in near real-time are revealed from the prediction results as early as possible.     

量子遗传算法在基于人工免疫的入侵检测系统中的应用

一个高效的入侵检测系统需要根据网络流的变化，不断对检测子进行优化。将新型的启发式方法——量子遗传算法应用于入侵检测系统中，并进行了仿真实验。结果表明：量子遗传算法在优化性能上优于传统遗传算法，它使种群具有多样性，提高了算法效率。     

改进量子遗传算法用于多峰值函数优化

传统遗传算法(SGA)在处理多峰值函数优化问题中存在局部收敛性的问题,最初的量子遗传算法(QGA)也存在这一问题。运用一种改进量子遗传算法(MQGA),有效地解决了一些多峰值函数的优化问题。根据几个重要的测试函数进行仿真实验结果证明,与SGA和QGA相比,改进的量子遗传算法(MQGA)在一些多峰值优化问题中更具有效性和可行性。     

基于量子遗传算法的盲源分离算法研究

在改进遗传量子算法的基础上，提出了一种新的量子遗传算法并从理论上证明了算法的全局收剑性．提出了基于量子遗传算法与独立分量分析算法相结合的盲源分离新算法．仿真结果表明：新方法比采用常规遗传算法的盲源分离方法具有明显的高效性．     

基于量子遗传算法的非线性无约束优化方法

量子遗传算法(QGA)是量子计算和遗传算法相结合的产物,量子遗传算法将量子比特和量子旋转门表示引入到遗传算法中,具有比遗传算法更好的搜索效率和收敛性。非线性无约束优化是典型的工程应用问题,而复杂非线性函数的优化结果往往不能令人满意,如陷入局部最优等。利用量子遗传算法强大的搜索能力,可以很好的解决复杂非线性函数的无约束优化问题,实验表明量子遗传算法在该类问题中的有效性和可行性。     

基于量子遗传算法的支持向量机人脸识别技术

首先应用K-L变换对人脸图像进行特征提取,然后利用支持向量机对其进行识别。由于支持向量机的参数对识别性能有较大影响,因此这篇文章文采用量子遗传算法对支持向量机参数进行选取。算法解决了支持向量机参数选取的难题。利用ORL人脸库进行仿真实验,得到了较好的识别效率。     

量子遗传算法在口腔种植定位中的研究与应用

口腔种植体设计方案的制定时间较长、智能程度不高,口腔医学数据信息量较大。为此,对种植体模型进行研究,提出一种改进的量子遗传算法。把种群细分为不同的特征群体,各特征群体实施自适应调整进化步长的量子旋转门操作,以及个体间信息交流的交叉操作。实验结果表明,与经典的遗传算法以及Bloch量子遗传算法相比,该算法能有效地优化种植体定位参数,搜索能力和收敛性能较好。     

一种改进型量子遗传算法

针对量子遗传算法在复杂连续函数优化中存在的收敛速度慢、易陷入局部极值等缺点,提出一种改进型量子遗传算法。采用动态策略调整量子门旋转角,以加快收敛速度,采用优体交叉策略实施交叉操作,以增强局部搜索能力。通过典型复杂连续函数的测试验证该算法的可行性和有效性。     

基于改进量子遗传算法的云计算资源调度

针对云计算环境下资源的高效调度问题,当前研究较少关注云服务提供商的服务成本,为此,以云服务提供商降低最小服务成本为目的,提出了改进量子遗传算法的云资源调度算法。由于采用二进制量子位表示的染色体无法描述资源调度矩阵,该算法将量子位的二进制编码转换为实数编码,并使用旋转策略和变异算子保证算法的收敛性。通过仿真实验平台将此算法与遗传算法和粒子群算法进行比较分析,在种群迭代次数为100的情况下,分别取种群数为1和10,实验结果表明该算法能取得更小的最小服务成本。     

量子遗传算法及其在多用户检测中的应用

量子计算与遗传算法相结合的量子遗传算法是当今计算智能领域的一个前沿课题。文章从研究遗传算法在并行实现上的困难和改进方法出发,探讨了量子计算与遗传算法结合的理论基础,分析了量子遗传算法的理论和结构,最后提出了一种基于量子遗传算法的多用户检测方法。仿真结果表明,基于量子遗传算法的多用户检测器抗多址干扰的能力明显优于经典遗传算法多用户检测器。     

基于k均值和量子遗传算法的RBF网络优化

针对遗传算法容易出现早熟的问题,提出一种基于k均值和量子遗传算法的径向基函数(RBF)神经网络组合优化方法.通过k均值聚类求取网络的中心,用量子遗传算法训练网络的权值,利用量子染色体的表示方式以及量子染色体的更新提高算法的并行性,从而解决遗传算法早熟的问题,提高网络的适应度.相对于PSO-RBF和ACO-RBF,该方法...