基于社会网络的舆情传播模型构建与分析

本文考虑个体间的相互作用,提出了一个基于社会网络分析的舆情传播模型.模型假设网络中每个个体的初始状态只有两种情况,并且在单位时间步内只选择一个邻居进行交流,并以概率λ接受该邻居的意见.解析结果显示存在概率λc使得网络中持某种观点人数比例的期望值是一个常数,而其它状态下系统中所有个体的意见都会达成一致.本模型有助于衡量某给定社会网络中舆论传播的快慢程度.     

基于事件驱动的环形编队多智能体系统

研究了控制器采用事件驱动机制时,多智能体系统的环形编队控制问题.事件驱动控制机制用来降低个体控制更新的频率,以此减少整个系统的能量消耗.假设所有智能体始终在一固定的圆环上运动,并且每个智能体只能感知到其前后两个紧邻智能体的信息.利用邻居的信息,对每个智能体设计分布式控制律,在事件驱动控制机制下,所提控制策略可以使多智能体系统形成给定的期望环形编队,并将环形编队扩展到特殊的均匀环形编队形式.数值的仿真验证了所提控制策略的有效性.     

一类不确定随机非线性系统的采样扩张状态观测器设计与观测误差的收敛性分析

实际系统中普遍存在各种干扰和不确定性因素,并且对控制系统的性能造成负面影响,因此设计对干扰和不确定性具有优异估计性能的观测器显得尤为重要.针对一类具有采样输出的不确定随机非线性系统,设计相应的采样扩张状态观测器用于在线估计不可量测状态和影响系统性能的随机总干扰.所估计的随机总干扰包含系统内部未建模动态、统计特性未知的外部有界噪声干扰以及不确定性因素的非线性耦合作用.在每个采样区间内,设计一个输出预估器用于估计实际输出,相应的输出估计值用于采样扩张状态观测器的设计.证明了所设计的采样扩张状态观测器对不可量测状态和随机总干扰的观测误差的均方收敛性.最后,一个具体仿真实例用于证实理论结果.     

相关噪声下多步无序量测状态估计更新算法

在多传感器系统中,由于通信时间的延迟性,常常会出现无序量测情况.为了提高估计精度,系统须对无序量测进行更新估计.状态估计更新算法是处理无序量测问题的一种有效方法.在过程噪声和量测噪声相关条件下,给出了含无序量测的传感器系统状态估计更新算法.仿真计算验证了该算法的有效性.     

离散时间大种群随机多智能体系统的线性二次分散动态博弈

研究具有耦合二次型随机性能指标的离散时间大种群随机多智能体系统的分散博弈问题.系统所受的噪声干扰为条件二阶矩有界的鞅差序列,比以往研究所考虑的高斯白噪声情形更具有广泛性.采用状态聚集方法构造了对种群状态平均的估计,基于Nash必然等价原理设计了分散控制律,并利用概率极限理论分析了闭环系统的稳定性和最优性.主要结果包括(1)证明了对种群状态的平均的估计在某种范数意义下的强一致性,即种群状态的平均与其估计值之间的误差在该范数意义下将随系统个体数N的增加几乎必然收敛于0;(2)证明了闭环系统的几乎必然一致稳定性,即系统的稳定性与种群个体数N无关;(3)证明了所设计的分散控制律是几乎必然渐近Nash均衡策略.     

一类非线性系统的迭代学习控制

讨论一类非线性系统的迭代学习控制,系统的非线性动态对状态不快于多项式增长,而量测方程含有噪声.控制序列并非直接输给系统,而是先经过死区、预载及饱和等非线性函数.递推地给出了学习控制序列,并证明它的有界性及最优跟踪性.     

一阶多智能体系统的一致性分析

研究一阶连续多智能体系统的一致性问题,其中每个智能体只能在一系列离散时刻上获得其相对邻居的状态信息,并且每个智能体的保持器的周期和采样器的周期是不同的.通过分析多智能体系统的稳定性,获得了一致性成立的充要条件,该条件揭示了交流拓扑、控制器增益、采样器的周期和保持器的周期的关系.最后,提供一个仿真例子以说明理论结果的有效性.     

时延混成系统的切换控制器合成

如何设计安全、可靠的信息物理融合系统是计算机科学和控制理论面临的一个重大挑战.时延现象在信息物理融合系统中普遍存在,时延对系统的稳定性、安全性和控制性能具有实质性影响.但是在已有时延系统验证和控制器合成的工作中往往忽略时延因素,这会导致在不考虑时延情况下能保证稳定和安全的系统在实际运行时因为时延原因而不再稳定和安全.因为时延使得系统的行为演化不仅与当前状态有关,还依赖于系统的历史状态,所以时延混成系统的验证和控制合成更加困难.本文研究信息物理融合系统在考虑时延情形下切换控制器合成问题,提出基于不变式生成技术的控制器合成方法.首先,利用谱分析和线性化技术将时延系统的微分不变式生成问题归结为有界时间的可达集计算问题;然后,提出基于抽象精化的算法计算时延系统有界时间可达集的上近似;最后,实现本文算法并使用实例验证该方法的有效性.     

由2~(1/2)引发的图形构造

<正>任何一个数学问题的陈述都是由一些题设条件(初始状态)和问题的要求(目标状态)两部分组成,它们在语言结构与逻辑上具有一定的形式,并且在知识结构上蕴含着一定的信息.这些信息往往隐含着如何从初始状态通向目标状态的启示.对于一些几何题,其中的一些"特殊条件"往往是解题的金钥匙.在这些所     

二次型minimax指标下的随机控制

本文讨论了状态初值的统计特性为未知时,在二次性能指标下最一般的线性随机控制问题,不同于通常的假设,这里不要求噪声的正态性和量测噪声的非退化性,对这样的系统,针对最不利的初值统计特性,本文给出了最优随机控制。     

Impulsive consensus in directed networks of identical nonlinear oscillators with switching topologies

In this paper, we investigate the problem of impulsive consensus of multi-agent systems, where each agent can be modeled as an identical nonlinear oscillator. Firstly, an impulsive control protocol is designed for directed networks with switching topologies based on the local information of agents. Then sufficient conditions are given to guarantee the consensus of the networked nonlinear oscillators. How to select the discrete instants and impulsive constants is also discussed. Numerical simulations show the effectiveness of our theoretical results.     

Decentralized iterative learning control for a class of large scale interconnected dynamical systems

The problem of decentralized iterative learning control for a class of large scale interconnected dynamical systems is considered. In this paper, it is assumed that the considered large scale dynamical systems are linear time-varying, and the interconnections between each subsystem are unknown. For such a class of uncertain large scale interconnected dynamical systems, a method is presented whereby a class of decentralized local iterative learning control schemes is constructed. It is also shown that under some given conditions, the constructed decentralized local iterative learning controllers can guarantee the asymptotic convergence of the local output error between the given desired local output and the actual local output of each subsystem through the iterative learning process. Finally, as a numerical example, the system coupled by two inverted pendulums is given to illustrate the application of the proposed decentralized iterative learning control schemes.     

Heterogeneous trading agents

In this article, we present a multiagent system (MAS) simulation of a financial market and investigate the requirements to obtain realistic data. The model consists of autonomous, interactive agents that buy stock on a financial market. Transaction decisions are based on a number of individual and collective elements, the former being risk aversion and a set of decision rules reflecting their anticipation of the future evolution of prices and dividends and the latter the information arriving on the market influencing the decision making process of each trader. We specifically look at this process and the following observations hold: The market behavior is determined by the information arriving at the market and agent heterogeneity is required in order to obtain the right statistical properties of the price and return time series. The observed results are not sensitive to changes in the parameter values. © 2003 Wiley Periodicals, Inc.     

离散事件系统结构化状态反馈控制逻辑

离散事件系统(Discrete Event System,DES)是从制造系统、通讯系统、资源管理系统和城市/空中交通系统等人工系统中抽象出来的.这种系统可认为是离散的(在时间上和状态空间内)、异步的(即是由事件驱动而不是由时钟驱动),并且一般是非确定性的,所以处理这类系统的方法与连续时间系统迥然不同.在已有的处理 DES 的方法中,Wonham 和他的学生所提出的一种概念模型方法已引起了广泛的注意.在这方法中,他们成功地将计算机科学的一些概念和方法引入到控制中,新领域的出现和新方法论的发展使控制科学产生了新的活力.     

Extended and unscented filtering algorithms using one-step randomly delayed observations

In this paper, the least squares filtering problem is investigated for a class of nonlinear discrete-time stochastic systems using observations with stochastic delays contaminated by additive white noise. The delay is considered to be random and modelled by a binary white noise with values of zero or one; these values indicate that the measurement arrives on time or that it is delayed by one sampling time. Using two different approximations of the first and second-order statistics of a nonlinear transformation of a random vector, we propose two filtering algorithms; the first is based on linear approximations of the system equations and the second on approximations using the scaled unscented transformation. These algorithms generalize the extended and unscented Kalman filters to the case in which the arrival of measurements can be one-step delayed and, hence, the measurement available to estimate the state may not be up-to-date. The accuracy of the different approximations is also analyzed and the performance of the proposed algorithms is compared in a numerical simulation example.     

Robust State-Feedback Controllability of Linear Systems to a Hyperplane in a Class of Bounded Controls

The paper deals with a linear time-dependent dynamic system with scalar control and input uncertainty (disturbance). Two admissible classes of input uncertainty realizations are considered: the class of measurable bounded functions and the class of measurable quadratically integrable functions. The problem to be studied is the existence of a state feedback control with measurable bounded time realizations transferring the system to a given hyperplane (a target set) from any initial position in a prescribed time for any admissible input uncertainty realization. Necessary and sufficient conditions for the existence of such a control are derived, based on the explicit construction of this control by using an auxilary zero-sum linear-quadratic differential game with a cheap control for the minimizing player. Examples illustrting the theoritical results are presented.     

Effects of system parameters on the optimal policy structure in a class of queueing control problems

This paper studies a class of queueing control problems involving commonly used control mechanisms such as admission control and pricing. It is well established that in a number of these problems, there is an optimal policy that can be described by a few parameters. From a design point of view, it is useful to understand how such an optimal policy varies with changes in system parameters. We present a general framework to investigate the policy implications of the changes in system parameters by using event-based dynamic programming. In this framework, the control model is represented by a number of common operators, and the effect of system parameters on the structured optimal policy is analyzed for each individual operator. Whenever a queueing control problem can be modeled by these operators, the effects of system parameters on the optimal policy follow from this analysis.      

Maximum-likelihood recursive nonlinear filtering

The basic model for the general nonlinear filtering problem consists of a nonlinear plant driven by noise followed by nonlinear observation with additive noise. The object is to estimate, at each instant, the current state of the plant, given thea priori information and the history of the observations up to the current time. The estimation procedure studied here is that of computing, at each instant, the most probable trajectory given the data at that time, and taking its final value. The purpose of the present paper is to clarify some earlier studies of this procedure.This research was partially supported by the National Science Foundation, Grant No. GK-806.     

On the Motion of Agents across Terrain with Obstacles

The paper is devoted to finding the time optimal route of an agent travelling across a region from a given source point to a given target point. At each point of this region, a maximum allowed speed is specified. This speed limit may vary in time. The continuous statement of this problem and the case when the agent travels on a grid with square cells are considered. In the latter case, the time is also discrete, and the number of admissible directions of motion at each point in time is eight. The existence of an optimal solution of this problem is proved, and estimates of the approximate solution obtained on the grid are obtained. It is found that decreasing the size of cells below a certain limit does not further improve the approximation. These results can be used to estimate the quasi-optimal trajectory of the agent motion across the rugged terrain produced by an algorithm based on a cellular automaton that was earlier developed by the author.     

Wavelet frame based blind image inpainting

Image inpainting has been widely used in practice to repair damaged/missing pixels of given images. Most of the existing inpainting techniques require knowing beforehand where those damaged pixels are, either given as a priori or detected by some pre-processing. However, in certain applications, such information neither is available nor can be reliably pre-detected, e.g. removing random-valued impulse noise from images or removing certain scratches from archived photographs. This paper introduces a blind inpainting model to solve this type of problems, i.e., a model of simultaneously identifying and recovering damaged pixels of the given image. A tight frame based regularization approach is developed in this paper for such blind inpainting problems, and the resulted minimization problem is solved by the split Bregman algorithm first proposed by Goldstein and Osher (2009) [1]. The proposed blind inpainting method is applied to various challenging image restoration tasks, including recovering images that are blurry and damaged by scratches and removing image noise mixed with both Gaussian and random-valued impulse noise. The experiments show that our method is compared favorably against many available two-staged methods in these applications.