广义离散随机线性系统的最优递推滤波方法(Ⅱ)

本文对文献[1]给出的广义离散随机线性系统最优估计误差协方差阵进行了分析,在一定 条件下得到了误差协方差阵的上界和下界,继而讨论了由文献[1]给出的滤波器的稳定性.     

时变概率随机拓扑条件下高阶群系统一致性分析

在时变连接概率的随机拓扑条件下, 研究了离散时间高阶线性群系统的一致性问题. 首先, 给出一个依赖于相邻主体间拓扑连接概率和各个主体自身信息的随机控制协议; 然后, 应用状态空间分解法分析离散时间高阶线性群系统的一致性, 给出了在连接概率时变的随机拓扑条件下以概率为1 实现一致的充分必要条件; 进而, 确定了随机拓扑条件下离散时间高阶线性群系统的一致函数; 最后, 通过数值分析验证了所得出结论的正确性.

     

概率指标的命中控制问题

本文研究了一种以命中概率为指标的随机最优控制模型。给出了该模型的动态规划方程.在一些条件下,证明了最优控制律为一非律性方程组的解。     

非最小相位线性系统的可辨识性及辨识方法——非平稳输入

非平稳过程在实际中是大量遇到的,该文研究当系统输入是不可量测的非平稳过程时,系 统相位的可辨识性及辨识方法.文中给出了一般性结论并与平稳条件下所得的结果[1]进行 了比较.对零初始条件下的渐近平稳过程这一非平稳特例做了较详细的研究给出了若干有意 义的结果.这些结果比较合理地解决了由系统的幅谱恢复非最小相位系统的相位信息的问 题.     

一般信息结构下的Robust控制器

本文在文献[1,2,3]的基础上,直接给出了一般信息结构(GIS)下,系统的一类Robust 控制器的结构、存在条件和设计方法,避免了用间接方法[4]的要增加输出向量维数和使观测 矩阵降秩的不足之处.推广了文献[1]和[2]的集中和块对角分散信息结构下的有关结论. 若所讨论的GIS是经济信息结构[5,6,7,8],则该GIS下的Robust控制器即为经济Robust控 制器.最后举例说明本文方法的应用.     

具有多个独立执行机构的Lurie控制系统的鲁棒绝对稳定性

本文讨论了具有多个相互独立执行机构的Lurie型区间直接控制系统和区间间接控制系统的绝对稳定性 ,给出了系统稳定的若干充分条件 ,对文 [5 ]所讨论的问题作了进一步探讨 .     

关于区间矩阵的稳定性

文献[1]试图给出由端点矩阵的稳定性来保证区间矩阵的稳定性,文[2,3]指出文[1]的主要结果是错的.本文给出了端点矩阵的稳定性在一定条件下可以保证区间矩阵的稳定性,其结果比文[4]更精确,适用范围更大,且对具有分解的区间矩阵给出了其稳定及不稳定的充分条件.     

基准设计问题的补充说明:实验条件

为了使不具备实验条件的作者也能够对论文提出的控制算法进行试验验证 ,深圳固高科技有限公司可提供实验结果[1] (如果作者本身具有固高科技有限公司所产的固高直线一级倒立摆实验设备 ,请在应征论文中给出实验结果 ) .鉴于固高直线倒立摆的模型参数以及执行机构与《鲁棒控制基准设计问题 :倒立摆控制》(以下简称《问题》)一文有异 ,现将实验条件描述如下 ,以做《问题》的补充 .希望固高公司提供实验的作者 ,以本文的数学模型以及参数为依据 ,在论文中给出控制器相应的修正方法 .1　执行机构《问题》中的系统的执行机构是直流伺服电机 .现在…     

基于信息熵理论的网络化集群系统的可靠性分析

首先, 从信息论的角度借用信道容量概念定义了网络化集群系统的可靠度模型; 然后, 在已知一个节点可靠度和链接概率分布的条件下, 基于所建立的网络化工程系统的可靠度模型, 通过对拉格朗日微分方程的求解, 给出了网络节点链接失败概率总和达到最大时, 单节点链接失败概率的计算方法; 在此基础上, 基于已知一个节点可靠度的条件, 通过微分方程给出了网络节点链接失败概率总和达到最小时单节点链接失败概率的计算模型; 最后, 给出了一些具有导向性的结论.     

带不确定量测和未知虚警概率的粒子滤波器

为了解决带不确定量测和未知虚警概率的非线性非高斯系统状态估计问题,本文提出了一种新的粒子滤波方法,利用随机不确定量测模型来更新粒子和权值,并基于极大似然准则来辨识未知的虚警概率.本文所提出的带不确定量测和已知虚警概率的粒子滤波方法与现有标准的粒子滤波方法具有几乎一致的计算复杂度,但是更适合用于处理带不确定量测的非线性非高斯系统状态估计问题.此外,在状态转移密度函数被选择为建议密度函数时,本文证明了基于所提出的虚警概率辨识方法的极大似然估计唯一,从而为精确辨识虚警概率提供了理论保证.单变量非平稳增长模型和纯方位跟踪的数值仿真验证了所提出粒子滤波方法的有效性和与现有方法相比的优越性.     

Parametric probabilistic transition systems for system design and analysis

We develop a model of parametric probabilistic transition Systems (PPTSs), where probabilities associated with transitions may be parameters. We show how to find instances of the parameters that satisfy a given property and instances that either maximize or minimize the probability of reaching a certain state. As an application, we model a probabilistic non-repudiation protocol with a PPTS. The theory we develop allows us to find instances that maximize the probability that the protocol ends in a fair state (no participant has an advantage over the others). A preliminary version of this paper was presented at SEFM’04 [LMT04]. 05 April 2006     

Optimizing returns with stock option strategies: An integer programming approach

The paper develops an integer programming model for obtaining the optimum number of call and put stock options to buy in order to maximize the expected profitability from the resulting straddle. Option values are obtained using the Black and Scholes model [1].The objective function of the model is based on probabilistic estimates rather than on an empirical utility function, as has been done in the past [7].All the input parameters to the model are observable with the exception of break even points, which are based on the subjective judgement of the investor and the historical behavior of the underlying security.     

Model checking for a probabilistic branching time logic with fairness

We consider concurrent probabilistic systems, based on probabilistic automata of Segala & Lynch [55], which allow non-deterministic choice between probability distributions. These systems can be decomposed into a collection of “computation trees” which arise by resolving the non-deterministic, but not probabilistic, choices. The presence of non-determinism means that certain liveness properties cannot be established unless fairness is assumed. We introduce a probabilistic branching time logic PBTL, based on the logic TPCTL of Hansson [30] and the logic PCTL of [55], resp. pCTL [14]. The formulas of the logic express properties such as “every request is eventually granted with probability at least p”. We give three interpretations for PBTL on concurrent probabilistic processes: the first is standard, while in the remaining two interpretations the branching time quantifiers are taken to range over a certain kind of fair computation trees. We then present a model checking algorithm for verifying whether a concurrent probabilistic process satisfies a PBTL formula assuming fairness constraints. We also propose adaptations of existing model checking algorithms for pCTL [4, 14] to obtain procedures for PBTL under fairness constraints. The techniques developed in this paper have applications in automatic verification of randomized distributed systems. Received: June 1997 / Accepted: May 1998     

Refinement-oriented probability for CSP

Jones and Plotkin give a general construction for forming a probabilistic powerdomain over any directed-complete partial order [Jon90, JoP89]. We apply their technique to the failures/divergences semantic model for Communicating Sequential Processes [Hoa85].The resulting probabilistic model supports a new binary operator, probabilistic choice, and retains all operators of CSP including its two existing forms of choice. An advantage of using the general construction is that it is easy to see which CSP identities remain true in the probabilistic model. A surprising consequence however is that probabilistic choice distributes through all other operators; such algebraic mobility means that the syntactic position of the choice operator gives little information about when the choice actually must occur. That in turn leads to some interesting interaction between probability and nondeterminism.A simple communications protocol is used to illustrate the probabilistic algebra, and several suggestions are made for accommodating and controlling nondeterminism when probability is present.All authors are members of the Programming Research Group; McIver and Seidel are supported by the EPSRC.0     

A Generative Probabilistic Oriented Wavelet Model for Texture Segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.     

METASYSTEMS AND THE MAXIMUM ENTROPY PRINCIPLE

Abstract

A probabilistic model of data that is an extension of the standard relational model predominant in database management applications is introduced in [Cavallo and Pittarelli. 1987] and elaborated in [Pittarelli, 1993 and 1994]. A probabilistic database instance is identical to what, in the GSPS hierarchy of systems types [Klir, 1985], is referred to as a probabilistic structure behavior system.

We discuss the problem of modifying a probabilistic structure system to accommodate new information. This corresponds to what, in the literature on databases, is referred to as an update. A sequence of probabilistic structure systems, each the result of modifying its predecessor, may be regarded as a metasystem [Klir, 1985]. We will focus on computational aspects of replacing one structure system with its successor. A distinction is made between global and local updating of a structure system, using Bayesian or quasi-Bayesian methods. A global update consists of selecting an element of the reconstruction family, updating it appropriately, then projecting. A local update propagates the effect of updating a single behavior function to the other elements of the structure system. It is shown that Bayesian local updating produces the same structure system that results when the maximum entropy element of the reconstruction family is selected as the basis for a Bayesian global update.     

SPOT Databases: Efficient Consistency Checking and Optimistic Selection in Probabilistic Spatial Databases

Spatial PrObabilistic Temporal (SPOT) databases are a paradigm for reasoning with probabilistic statements about where objects are now or in the future. They express statements of the form "Object O is in spatial region R at time t with some probability in the interval [L,U]." Past work on SPOT databases uses selection operators returning SPOT atoms entailed by the SPOT database - we call this "cautious" selection. In this paper, we study several problems. First, we introduce the notion of "optimistic" selection queries that return sets of SPOT atoms consistent with, rather than entailed by, the SPOT database. We then develop an approach to scaling SPOT databases that has three main contributions: (i) We substantially reduce the size of past work's linear programs via variable elimination. (ii) We rigorously prove how one can prune the space searched in optimistic selection. (iii) We build an efficient index to execute optimistic selection queries over SPOT databases. Our approach is superior to past work in two major respects: first, it makes fewer assumptions than all past works on this topic except [30]. Second, the experiments - some using real world ship movement data - show substantially better performance than achieved in [30].     

大系统动态递阶控制设计

本文在[1]的模型基础上,研究了当系统具有二次性能指标时如何设计动态递阶控制问题,提出了动态递阶控制模型和三级递阶算法,并详细讨论了它的性质和收敛性问题,该模型和算法克服了文献[2]中的缺陷。     

Modeling and Analysis of Data Flow-Oriented ROS2 Data Distribution Service

Robot Operating System (ROS) is an open-source, meta-operating system, which can provide a structured communication layer based on the message mechanism on heterogeneous computing clusters. To improve the unsatisfactory real-time performance and reliability of data distribution in ROS1, ROS2 is proposed with a data flow-oriented data distribution service mechanism. This study validates the real-time performance and reliability of the ROS2 data distribution mechanism by means of probabilistic model checking. Firstly, a formal validation framework is put forward for the data flow-oriented ROS2 data distribution service system, and the probabilistic timed automata model is set up for the communication system module. Secondly, the probabilistic model checker PRISM is used to verify the real-time performance and reliability of data flow-oriented ROS2 data distribution service through the analysis of data loss rate and system response time. Finally, depending on the retransmission mechanism and Quality of Service (QoS) policy analysis, different data requirements and quantitative performance analysis of the transmission mode are achieved through the setting and adjustment of QoS parameters. This study can provide references for ROS2 application designers and the formal modeling, validation, and quantitative performance analysis of the distributed data distribution service based on the data flow.     

Annotated Logic Applications for Imperfect Information

Imperfect information is a very general term that comprises different types of information, such as uncertain, vague, fuzzy, inconsistent, possibilistic, probabilistic, partially or totally incomplete information [2]. In the literature of knowledge representation we find a different formal model for each one of these distinct types. For example, annotated logic is a formal model to represent inconsistent information.Annotated logics are non-classical logics introduced in [20] as a logic programming theory. They were proved to be paraconsistent. Based on [5], we present in this work the annotated logic programming theory and some of its applications in Artificial Intelligence (AI). We present it as a formalism to reason with inconsistent information and investigate its possibility to represent other types of imperfect information, such as possibilistic and non-monotonic reasoning. Our main goal is to verify and confirm the importance of annotated logics as a tool for developing knowledge-based and automated reasoning systems in AI.