具有随机协议网络化系统的H_∞滤波

本文研究了一类具有随机介质访问协议网络化系统的H∞滤波问题.将传感器和滤波器的通信过程描述为一个马尔可夫链,进而将滤波误差系统建模成一个马尔可夫跳变系统.然后,运用李雅普诺夫方法和线性矩阵不等式技术,给出了滤波误差系统随机稳定且具有给定H∞性能的一个充分条件,并基于该条件给出了H∞滤波器的设计方法.最后的数值算例验证了本文方法的有效性.     

一种粒子滤波的改进算法

在强非线性、非高斯系统、高精度测量的环境下,针对粒子滤波（PF）算法的跟踪性能降低问题,提出一种PF的改进算法。由于PF算法的计算量虽然小但精度不高,而无迹粒子滤波（UPF）算法精度虽然很高但计算量过大,结合PF算法计算量小和UPF算法精度高的优势,提出一种PF改进算法。对PF、UPF和PF改进算法三种跟踪算法进行了仿真,结果表明,改进PF算法的跟踪精度和UPF的跟踪精度相当,但所需运算时间仅为UPF算法的35%左右。     

Mean square stability for Kalman filtering with Markovian packet losses

This paper studies the stability of Kalman filtering over a network subject to random packet losses, which are modeled by a time-homogeneous ergodic Markov process. For second-order systems, necessary and sufficient conditions for stability of the mean estimation error covariance matrices are derived by taking into account the system structure. While for certain classes of higher-order systems, necessary and sufficient conditions are also provided to ensure stability of the mean estimation error covariance matrices. All stability criteria are expressed by simple inequalities in terms of the largest eigenvalue of the open loop matrix and transition probabilities of the Markov process. Their implications and relationships with related results in the literature are discussed.     

量子投影滤波

量子滤波器基于贝叶斯原理,利用连续弱测量数据给出当前时刻量子系统状态的最优估计,是量子计算和量子调控技术中极为重要的一环.然而,随着量子系统能级数提高,量子滤波器的实时计算复杂度呈二次型增长.本文介绍了一种量子投影滤波方法,用于减少量子滤波器的实时计算复杂度.基于量子信息几何方法,量子轨迹被限制在了一个由一类非归一化量子密度矩阵组成的子流形中.量子态从而可通过计算子流形的坐标系统来近似获得.仿真实验说明了投影滤波方法的有效性.     

Quantum estimation,control and learning: Opportunities and challenges

The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.     

A Quantum Monte Carlo method at fixed energy

In this paper we explore ways to study the zero temperature limit of quantum statistical mechanics using Quantum Monte Carlo simulations. We develop a Quantum Monte Carlo method in which one fixes the ground state energy as a parameter. The Hamiltonians we consider are of the form H=H₀+λV with ground state energy E. For fixed H₀ and V, one can view E as a function of λ whereas we view λ as a function of E. We fix E and define a path integral Quantum Monte Carlo method in which a path makes no reference to the times (discrete or continuous) at which transitions occur between states. For fixed E we can determine λ(E) and other ground state properties of H.     

Quantized filtering for Markovian jump LPV systems with intermittent measurements

This paper investigates the problem of quantized filtering for a class of discrete‐time linear parameter‐varying systems with Markovian switching under data missing. The measured output of the plant is quantized by a logarithmic mode‐independent quantizer. The data missing phenomenon is modeled by a stochastic variable. The purpose of the problem addressed is to design a full‐order filter such that the filtering error dynamics is stochastically stable and the prescribed noise attenuation level in the sense can be achieved. Sufficient conditions are derived for the existence of such filters in terms of parameterized linear matrix inequalities. Then the corresponding filter synthesis problem is transformed into a convex optimization problem that can be efficiently solved by using standard software packages. A simulation example is utilized to demonstrate the usefulness of the developed theoretical results. Copyright © 2011 John Wiley & Sons, Ltd.     

Robust Kalman filtering for uncertain systems

This paper studies the problem of Kalman filter design for uncertain systems. The system under consideration is subjected to time-varying norm-bounded parameter uncertainties in both the state and measurement matrices. The problem we address is the design of a state estimator such that the covariance of the estimation error is guaranteed to be within a certain bound for all admissible uncertainties. A Riccati equation approach is proposed to solve the above problem. Furthermore, a suboptimal covariance upper bound can be computed by a convex optimization.     

量子力学和量子计算机

In this paper,the relationship between computation and physics and the application of the principle of Quantum mechanics to Quantum Computing and Quantum Computers was reviewed     

Approximate finite-dimensional filtering for polynomial states over polynomial observations

In this article, the mean-square filtering problem for polynomial system states over polynomial observations is studied proceeding from the general expression for the stochastic Ito differentials of the mean-square estimate and the error variance. In contrast to the previously obtained results, this article deals with the general case of nonlinear polynomial states and observations. As a result, the Ito differentials for the mean-square estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining an approximate closed-form finite-dimensional system of the filtering equations for any polynomial state over observations with any polynomial drift is then established. In the example, the obtained closed-form filter is applied to solve the third-order sensor filtering problem for a quadratic state, assuming a conditionally Gaussian initial condition for the extended third-order state vector. The simulation results show that the designed filter yields a reliable and rapidly converging estimate.     

Quantum models of cognition and decision

Abstract

The purpose of this article is to discuss principle ideas of quantum cognition research program, which comprise elements of the formalism of quantum mechanics (mainly Hilbert space theory and quantum probability theory) for modeling human cognition and decision processes. In the opinion of authors of this program, paradox empirical findings in psychological literature may be explained based on concepts of quantum mechanics. Formally, there is described a discrete-time random chain χ which is defined on a finite interval [0, T] and χ(t) can assume only finite number of values. The space H of such processes will be finite-dimensioned. Then some properties and applications of the quantum probability space on H are studied.     

Quantum Computing and Information Extraction for Dynamical Quantum Systems

We discuss the simulation of complex dynamical systems on a quantum computer. We show that a quantum computer can be used to efficiently extract relevant physical information. It is possible to simulate the dynamical localization of classical chaos and extract the localization length with quadratic speed up with respect to any known classical computation. We can also compute with algebraic speed up the diffusion coefficient and the diffusion exponent, both in the regimes of Brownian and anomalous diffusion. Finally, we show that it is possible to extract the fidelity of the quantum motion, which measures the stability of the system under perturbations, with exponential speed up. The so-called quantum sawtooth map model is used as a test bench to illustrate these results. PACS: 03.67.Lx, 05.45.Mt     

H∞ filtering for singular Markovian jump systems with time delay

The problem of H_∞ filtering is considered for singular Markovian jump systems with time delay. In terms of linear matrix inequality (LMI) approach, a delay‐dependent bounded real lemma (BRL) is proposed for the considered system to be stochastically admissible while achieving the prescribed H_∞ performance condition. Based on the BRL and under partial knowledge of the jump rates of the Markov process, both delay‐dependent and delay‐independent sufficient conditions that guarantee the existence of the desired filter are presented. The explicit expression of the desired filter gains is also characterized by solving a set of strict LMIs. Some numerical examples are given to demonstrate the effectiveness of the proposed methods. Copyright © 2009 John Wiley & Sons, Ltd.     

On the combination of user-based and item-based collaborative filtering

In this paper, we propose two new filtering algorithms which are a combination of user-based and item-based collaborative filtering schemes. The first one, Hybrid-Ib, identifies a reasonably large neighbourhood of similar users and then uses this subset to derive the item-based recommendation model. The second algorithm, Hybrid-CF, starts by locating items similar to the one for which we want a prediction, and then, based on that neighbourhood, it generates its user-based predictions. We start by describing the execution steps of the algorithms and proceed with extended experiments. We conclude that our algorithms are directly comparable to existing filtering approaches, with Hybrid-CF producing favorable or, in the worst case, similar results in all selected evaluation metrics.     

确保估计性能的离散Markov跳跃系统鲁棒Kalman滤波

本文研究一类具有不确定噪声的离散时fnqMarkov跳跃线性系统的鲁棒Kalman滤波器设计问题.文中基于确保状态估计误差性能指标的原理,给出了不确定噪声协方差矩阵的扰动上界,并在此界限内采用最坏情况下的最优滤波器实现对状态的估计.该设计方案不仅能极小化不确定下的最坏性能.而且能够确保性能指标达到给定的某个自由度.文中给出数值算例表明了设计方案的有效性.     

Exploiting user feedback for online filtering in event-based systems

Modern large-scale internet applications represent today a fundamental source of information for millions of users. The larger is the user base, the more difficult it is to control the quality of data that is spread from producers to consumers. This can easily hamper the usability of such systems as the amount of low quality data received by consumers grows uncontrolled. In this paper we propose a novel solution to automatically filter new data injected in event-based systems with the aim of delivering only content consumers are actually interested in. Filtering is executed by profiling producers and consumers, and matching their profiles as new data is produced. Profiles are built by aggregating feedback submitted by consumers on previously received data.     

改进的模糊遗传算法及在信息过滤中的应用

为了改进传统遗传算法在求解复杂问题上存在早收敛及搜索后期运行效率低等缺点,提出了一种应用于文本分类和信息过滤的模糊遗传算法.首先应用了年龄概念来控制种群规模,使得遗传操作过程更接近于自然进化过程,然后引进参数的模糊调整过程,对遗传算法的参数种群规模,交叉率及变异率3个方面进行动态调整,改进了遗传算法的搜索性能.实验结果表明,相比传统遗传算法,该模糊遗传算法在全局优化能力及收敛速度上均有显著提高.     

Kalman filtering with partial Markovian packet losses

We consider the Kalman filtering problem in a networked environment where there are partial or entire packet losses described by a two state Markovian process. Based on random packet arrivals of the sensor measurements and the Kalman filter updates with partial packet, the statistical properties of estimator error covariance matrix iteration and stability of the estimator are studied. It is shown that to guarantee the stability of the Kalman filter, the communication network is required to provide for each of the sensor measurements an associated throughput, which captures all the rates of the successive sensor measurements losses. We first investigate a general discrete-time linear system with the observation partitioned into two parts and give sufficient conditions of the stable estimator. Furthermore, we extend the results to a more general case where the observation is partitioned into n parts. The results are illustrated with some simple numerical examples.     

广域推荐:社会网络与协同过滤(英文)

商务企业应用数据挖掘技术向潜在客户推荐产品。大多数推荐系统聚焦研究兴趣于特定的领域,如电影或书籍。使用用户相似度或产品相似度的推荐算法通常可以达到较好效果。然而,当面临其他领域问题时,推荐常变得非常困难,因为数据过于稀疏,难以仅基于购买历史发现用户或产品间的相似性。为解决此问题,提出使用社会网络数据,通过对历史的观察提高产品推荐有效性。利用人工协同过滤和基于社会网络的推荐算法的最新进展进行领域推荐工作。研究显示社会网络的应用对于产品推荐具有很强的指导作用,但是,高的推荐精度需以牺牲召回率为代价。数据的稀疏性意味着社会网络并不总是可用,在这种情况下提出一种解决方案,很好地利用了社会网络的有效信息。     

基于相关滤波与颜色概率模型的目标跟踪算法

针对地面战场环境下相似背景对目标跟踪器产生的干扰，提出了一种基于相关滤波与改进颜色概率模型的目标跟踪算法。首先，在传统颜色概率模型的基础上，利用前景目标直方图与背景直方图的差异性提出了一种突出前景的颜色概率模型；然后，根据相关滤波器响应置信度和最大响应位置生成空间惩罚矩阵，用该矩阵惩罚相关滤波器判定的背景像素的似然概率，利用积分图的方法得到颜色概率模型响应图；最后，将相关滤波器和颜色概率模型得到的响应图进行融合，融合响应图的最大响应位置即为目标的中心位置。与核循环结构滤波器(CSK)、核相关滤波器(KCF)、判别式尺度空间跟踪（DSST）、SAMF、Staple等5种算法在跟踪性能上进行比较，在OTB-100标准数据集上的结果表明，所提算法的整体精度提高了3.06%至55.98%，成功率提高了2.24%至54.97%；在相似背景干扰下，其精度提高了10.28%至43.9%，成功率提高了8.3%至48.29%。在36段战场视频序列上的结果表明，所提算法的整体精度提高了2.2%至45.98%，成功率提高了3.01%至58.27%。该算法能够更好地应对地面战场环境下相似背景的干扰，为武器平台提供更精确的位置信息。