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粗糙集理论与方法

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张文修的一本比较经典的粗糙集理论的教材,感兴趣的可以参考下00140230西安交通大学数学研究生教学丛书粗糙集理论与方法张文修吴伟志梁吉业李德玉编著2001内容简介本书系统地介绍了粗糙集理论的基本内容与方法,力图概括回内外最新成果主要内容有粗糙集的基本概念,粗糙计算方法,粗糙集的代数性质与粗糙逻辑,粗幡集的各种推广模型,粗糙集与其他处理不确定或不精确问邀理论的联系以不完备信息系统下的粗糙集方法本书可作为计算机科学应用数学、自动控制、信息科学和管理工程等专业的高年级学生及研究生的教材,也可作为研究粗橢集理论与方法的科技人员的参考书书在版编目CI据粗糙集理论与方法/文修等编著.北京:科学出版社,2001酉安交道大学数学研究生教学丛书)1sBN70307984.租…山.张…Ⅲ.粗糙集Ⅳ.Ol44中图娅本图书馆CIP数据校字(2000第69236号科學当腹越出版北京东监域根北]6号鄙蝙;117斯音刮厂郾刷科学出版社发行各她新华书店经销200H年月第版开本:F5(72×1020年7月第一次印剧印张:1434型数:1-3000字数:25100定价:22.00元(如有印质量间题,我社负资调换〈新欣当今,社会巳经齿入了恻络信息时代,计算机与网络信息技术的飞速发展使得各个领域的数据和信息急剧增加(信息爆炸),并且由于入类的参与使数据与信息系统中的不确定性更加显著(复杂系统)如何从大量的、杂乱无章的、强一扰的数据(海量效据)中挖掘潜在的、有利用价值的信息(有用知识这给人类的智能信息处理能大提出了所未有的挑战.由此产生了人工智能併究的一个崭新领城——数据挖掘(ⅠM和数据库知识发现(KDD在IM和KD诸多方法中,粗糙集理论与方法对于处理复杂系统不失为一种较为有效的方法,因为它与概率方法模糊集方法和证据理论方法等其他处理不确定性问题理论的最显著约区别是它无需提供问题所需处理的数据集合之外的任何先验信息当然,由于该理论未能包含处理不精确或不确定原始数据的机制,所以与其他处理不确定性问题的理论有很强的互补性相糙集理论是波兰数学家 Z Pawiak于1982年提出的一种数据分析理论由于最初关于粗糙集理论的研究主要集中在波兰,因此当时并没有引起国际计算机界和数学界的重视,研究地域仅局限于东欧一些国家.直到1990年前后,由于该理论在数据的决策与分析、模式识别、机器学习与知识发现等方面的成功应用,才逐渐引起了世界各国学者的广泛关注.1991年 Z Pawlak的专著《料糙集—关于数据推理的理论》 Rough: Sets-- -Thearetical/etsof Reasoning about Data)的问世,标志着粗糙集理论及其应用的研究进人丁活跃时期.1992年在波兰召开了关于粗糙集理论的第一屈国际学术会议.1995年A(M(απ munication将粗糙集列为新浮现的计算机科学的研究课题.目前粗糙集理论已成为信息科学最为活跃的研究领域之一,同时,该理论还在医学、化学、材料学地理学管理科学和金融等其他学科得到∫成功的应用本书的目的是介绍粗糙集射基本理论与方法以及这理论的研究发展状况.为了闯读方倜,本书对国内外已发表的文章进行了系统化处理,规范了数学概念与符号,在统一的框架下叙述了粗糙集理论的最新研究成果,同时也包含了作者的某些新成果,期望为从事粗糙集理论研究入员和研究生进人这新领域提供捷径鉴于我们从事该领域的研究工作时间较短,加之身知识的局限性,错误与不妥之处在所难免,热忧欢迎广大同仁批评、指止作者2000年8月录第-章粗糙集理论的基本概念§【.1知识与知识库§【.2不精确范嗨,近似与粗糙集…■■■■■■■■§.3知识约简……§1.4知识的依赖性………………………………………16§1.5知识表达系统17§.6决策表『·「TT·■冒■音T曾■鲁?1音曾■上……………19§1.7区分矩阵与区分函数笫二章粗糙集模型的算法262.1信总系统和决策表TT1T1冒量26§22简单分类27氵2.3支持子集………s24决策属性的支持度………kd■p电■山白山§2.5交的计算……………33s26多个条件的支持度■『■冒■■■卩甲■罩卩『■■■b■■d■b山I凸晶d■■34氵2.7函数依赖…………………35§2.8恒等依赖甲干·!■■■冒■1■dh十■m§2.9重要性和核§2.10属性依颊性T甲“■·T曾冒會會十個ql早4■■■個會3§2.11约简T■■第三章般关系下的粗糙集模型…§3.1二元关系与邻城算子……………41§3.2二元关系与粗糙近似算子…43§3,3近似算子的其地定义形式与比较……………4§34近似算子的表示…自■■■■■■4■郾LI卜郾4■■b▲■■■■■■■·甲聊a■b■着郾山晶d§3.5程度粗榧集模型…■■會會■■‘自自自■聊即聊■b■■当dh_画第四章粗糙集代数的公理化方法…*574.1粗糙集理论的构造性方法…rr…"w…5784.2粗糙集理论的公理化方法§4.3构造性方法与公理化方法的关系…………■·■幽日··■■口■甲■【山■中中…6284.4特殊类型的粗糙集代数第五章粗糙集系统的代数结构·「丬■"■·白幽■日■『■早■卜P画■着■昌白晶画聊甲嵋目录§5.1粗糙集的Se代数§5.2粗糙近似宰间血d幽唱幽日日4:bq1即4日日B:甲44日b·甲日甲4:·甲4§5.3粗集和 Nelson代数…■_L啁↓■■■■■b§5.4粗糙概念的代数刻画■■■■■■■■■■■d口口……………85§5.5半群中的粗理想……,……………■■■■93第六章粗糙逻辑与决策■■■■■■■歌■↓■■罩↓卩■l■■罩d■b■■鄢↓■k↓db■■■■b■kd看■郾■■b矗■司■山山d■b古■■98§6,1基于完备信息系统的粗逻辑986.2决策逻辑与决策………………1"""…!…"……s…100§6.3基于不光备信息系统的模态逻辑………………115第七章变榇度粗糙集模型■【■■冒■■甲卓■■■■山d血血個■备量§7.多燃包含关系…123§72咄精度粗槌集模型中的近似集……………………………………124§73集合钓相对可辨别性…………………………-:126§74B近似的性质…128属性钓近似依赖性129§7.6近似约简…甲甲■■■郾通4阝………",130第八章概率粗糙集模型132§8有限论域上概率测度的基本知识……13§8,2信息熵…L唱■LLa133§8.3概卒粗糙集模型∵……T■■■■■■…135§8.4概率粗糙集模型的其他形式1398.5Rys决策与粗糙近似142路呂.6粗糙隶属函数与概念的联合rr1148§8.7知识的不确定性度量§B8概率粗糧集模翘和确定性粗糙集模型的比较………,155第九章模糊粗糙集模型P■s…1589.1模糊集的基本慨念158§9,2糢糊关系………………441·日·日q甲日■_日面如a甲qrpa4P自……·160§93模糊粗糙集………161§9.4甚于三角模的模糊粗欖集模型…:16889.5基于包含度的粗牲集模型……………■■和冒省●·■口■即甲看看D品J§9.6絛正型模糊粗糙集模型……■;;■■山晶;aq41即■血mm■甲甲唱1酥晶日H甲■182§9.7粗糙集与模糊集的比较■■185第十章基于随机集的粗糙集模型187§0,1随机集容度泛函t87§10.2信任函数与似然函数…d幽··『看■备如▲■p甲甲4即申日■鲁自中■暴即l88§10.3基于随机集的粗糙集模型…T·「·■■『■■■■■■Lpd■b10.4近似算子与可能性测度………"…201第十一章不完备信息系统的粗糙集方法……*………………20811.]不完备信息系统忄·■曾■■■·◆I會■■P■冒■鲁會◆4l■§112近似集2078113决策表,决策规则和知识约简……208A11.4区分函数与约简的计算司甲甲■鲁甲甲■■■p211参考文献十個■■1幽"b■213记号表………………….223第一章粗糙集理论的基本概念粗糙集理论是一·种新的处理模糊和不确定性知识的数学工具,其主要思想就是在保持分类能力不变的前提下,通过知识约简,导出问题的决策或分类规则.目前,粗集理论已被成功地应用于机器学习、决策分析、过程控制、模式识別与数据挖掘等领域.夲章介绍标准粗糙集理论( Pawlak粗糙集模型}的基本概念,作为后面各章节的基础§1.1知识与知识库投U≠是找们感兴趣的对象成的有限集合,称为论域任何子集X匚U称为U中的个概念或范畴.为规范化起见,我们认为空集也是一个概念,U中的任何概念族称关于U的抽象知识,简称知识本书上要是对在U上能形成划分的那些知识感兴趣.一个划分定义为:价=X1,X2,…,Xn1;XCU,X;≠x,X∩X=,对于i≠j,,1,2U上的族划分称为X于U的个知认库( knowledge base设R是U上的一个等价关系,U/R表示R的所有等价类(或者U上的分类构成的集合,x]R表示包含元素∈I的R等价类…个知识库就是个关系系统K=(UR),其中U为非空有限集,称为论域R是U上的一族等价关系若PCR,且P≠分,则∩P(P中所有等价关系的交集)也是一个等价关系,称为P上的不可区分〔 ndis nihility)关系,记为ind(P),且有n(P)REP这样,Und(P)(即等价关系ind(P)的所有等价美)表示与等价关系族P相关的知识,称为K中关于U的P基本知识(P基本集)为单起鬼,我们用U代替Und(P),ind(P)的等价类称为知识P的基本概念或基本范畴特别地,如果Q∈R,则称Q为K中关于U的Q初等知识,Q的等价类为知识R的Q初等概念或Q初等范畴事实上,P基本范畴是拥有知识P的论域的基本特性换句话说它们是知识的堪本模块同样,我们也可定义:当K=(,R)为一个知识库,ind(K)定义为K中第一章粗糙集埋论的基本概怠所有等价关系的族,记作ind(K)“ind(P)≠PR例1.1绘定一玩具积木的集合U={x1,x2,…,xg},并假设这些积木有不同的颜色(红、黄、蓝),形状(方,圆、三角},体积(小,大).因此,这些积木都可以用颜色形状体积这些知识来描述例如一块积木可以是红色、小而圆的,或黄色、人而方的等如果我们根据某属性描述这些积木的情况,就可以按颜色、形状、体积分类按颜色分类:17337蓝了5;6"一黄按形状分类圆方℃34丁·8角按体积分类大I5,2a换言之,我们定义三个等价关系(即属性):颜色R1,形状R2和体积R3,通过这些等价关系,可以得到下而三个等价类UR1=1{x1,x3,xy},{x25;吧U/R2=1x1,xs,x2,x6},x3,x4,x,!},夏/R3={x2,x7,x81,{x1,x3,x4,x,6这些等价类是由知识库K=(U,R1,R2,R3})中的初等概念(初等范畴)构成的基本范畴是初等范畴的交集构成的,例如下列集合3,x7}∩:x3,x4,3+74{∩{x256783y丁4;了它们分别为R1,R2}的基本范畴,即:红色三角形,蓝色方形,黄色三角形下列集合x3,x?C「x3,x4,x5,xs∩2,7x8={72,x1∩x,x;6∩2,x7,x8}={x2},5x69E845778f它们分别为{R12R2,R3的基本范畴,即红色大三角形,蓝色大方形,黄色大

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Michaeli and yonina c. eldar8.1 Introduction2668.2 Notation and mathematical preliminaries2688.3 Sampling and reconstruction setup2708.4 Optimization methods2788.5 Subspace priors2808.6 Smoothness priors2908.7 Comparison of the various scenarios3008.8 Sampling with noise3028. 9 Conclusions310Acknowledgments311References311Robust broadband adaptive beamforming using convex optimizationMichael Rubsamen, Amr El-Keyi, Alex B Gershman, and Thia Kirubarajan9.1 Introduction3159.2 Background3179.3 Robust broadband beamformers3219.4 Simulations330Contents9.5 Conclusions337Acknowledgments337References337Cooperative distributed multi-agent optimization340Angelia Nedic and asuman ozdaglar10.1 Introduction and motivation34010.2 Distributed-optimization methods using dual decomposition34310.3 Distributed-optimization methods using consensus algorithms35810.4 Extensions37210.5 Future work37810.6 Conclusions38010.7 Problems381References384Competitive optimization of cognitive radio MIMO systems via game theory387Gesualso Scutari, Daniel P Palomar, and Sergio Barbarossa11.1 Introduction and motivation38711.2 Strategic non-cooperative games: basic solution concepts and algorithms 39311.3 Opportunistic communications over unlicensed bands411.4 Opportunistic communications under individual-interferenceconstraints4151.5 Opportunistic communications under global-interference constraints43111.6 Conclusions438Ackgment439References43912Nash equilibria: the variational approach443Francisco Facchinei and Jong-Shi Pang12.1 Introduction44312.2 The Nash-equilibrium problem4412. 3 EXI45512.4 Uniqueness theory46612.5 Sensitivity analysis47212.6 Iterative algorithms47812.7 A communication game483Acknowledgments490References491Afterword494Index49ContributorsSergio BarbarossaYonina c, eldarUniversity of rome-La SapienzaTechnion-Israel Institute of TechnologyHaifaIsraelAmir beckTechnion-Israel instituteAmr El-Keyiof TechnologyAlexandra universityHaifEgyptIsraelFrancisco facchiniStephen boydUniversity of rome La sapienzaStanford UniversityRomeCaliforniaItalyUSAAlex b, gershmanTsung-Han ChanDarmstadt University of TechnologyNational Tsing Hua UniversityDarmstadtHsinchuGermanyTaiwanYongwei HuangTsung-Hui ChangHong Kong university of scienceNational Tsing Hua Universityand TechnologyHsinchuHong KongTaiwanThia KirubarajanChong-Yung chiMcMaster UniversityNational Tsing Hua UniversityHamilton ontarioHsinchuCanadaTaiwanZhi-Quan LuoJoachim dahlUniversity of minnesotaanybody Technology A/sMinneapolisDenmarkUSAList of contributorsWing-Kin MaMichael rebsamenChinese University of Hong KongDarmstadt UniversityHong KonTechnologyDarmstadtAntonio de maioGermanyUniversita degli studi di napoliFederico iiGesualdo scutariNaplesHong Kong University of Sciencealyand TechnologyHong KongJacob MattingleyAnthony Man-Cho SoStanford UniversityChinese University of Hong KongCaliforniaHong KongUSAJitkomut songsinTomer michaeliUniversity of californiaTechnion-Israel instituteLoS Angeles. CaliforniaogyUSAHaifaMarc teboulleTel-Aviv UniversityAngelia NedicTel-AvUniversity of Illinois atIsraelUrbana-ChampaignInoSLieven VandenbergheUSAUniversity of CaliforniaLos Angeles, CaliforniaUSAAsuman OzdaglarMassachusetts Institute of TechnologyYue WangBoston massachusettsVirginia Polytechnic InstituteUSAand State UniversityArlingtonDaniel p palomarUSAHong Kong University ofScience and TechnologyYinyu YeHong KongStanford UniversityCaliforniaong-Shi PangUSAUniversity of illinoisat Urbana-ChampaignShuzhong zhangIllinoisChinese university of Hong KongUSAHong KongPrefaceThe past two decades have witnessed the onset of a surge of research in optimization.This includes theoretical aspects, as well as algorithmic developments such as generalizations of interior-point methods to a rich class of convex-optimization problemsThe development of general-purpose software tools together with insight generated bythe underlying theory have substantially enlarged the set of engineering-design problemsthat can be reliably solved in an efficient manner. The engineering community has greatlybenefited from these recent advances to the point where convex optimization has nowemerged as a major signal-processing technique on the other hand, innovative applica-tions of convex optimization in signal processing combined with the need for robust andefficient methods that can operate in real time have motivated the optimization commu-nity to develop additional needed results and methods. The combined efforts in both theoptimization and signal-processing communities have led to technical breakthroughs ina wide variety of topics due to the use of convex optimization This includes solutions tonumerous problems previously considered intractable; recognizing and solving convex-optimization problems that arise in applications of interest; utilizing the theory of convexoptimization to characterize and gain insight into the optimal-solution structure and toderive performance bounds; formulating convex relaxations of difficult problems; anddeveloping general purpose or application-driven specific algorithms, including thosethat enable large-scale optimization by exploiting the problem structureThis book aims at providing the reader with a series of tutorials on a wide varietyof convex-optimization applications in signal processing and communications, writtenby worldwide leading experts, and contributing to the diffusion of these new developments within the signal-processing community. The goal is to introduce convexoptimization to a broad signal-processing community, provide insights into how convexoptimization can be used in a variety of different contexts, and showcase some notablesuccesses. The topics included are automatic code generation for real-time solvers, graphical models for autoregressive processes, gradient-based algorithms for signal-recoveryapplications, semidefinite programming(SDP)relaxation with worst-case approximationperformance, radar waveform design via SDP, blind non-negative source separation forimage processing, modern sampling theory, robust broadband beamforming techniquesdistributed multiagent optimization for networked systems, cognitive radio systems viagame theory, and the variational-inequality approach for Nash-equilibrium solutionsPrefaceThere are excellent textbooks that introduce nonlinear and convex optimization, providing the reader with all the basics on convex analysis, reformulation of optimizationproblems, algorithms, and a number of insightful engineering applications. This book istargeted at advanced graduate students, or advanced researchers that are already familiarwith the basics of convex optimization. It can be used as a textbook for an advanced graduate course emphasizing applications, or as a complement to an introductory textbookthat provides up-to-date applications in engineering. It can also be used for self-study tobecome acquainted with the state of-the-art in a wide variety of engineering topicsThis book contains 12 diverse chapters written by recognized leading experts worldwide, covering a large variety of topics. Due to the diverse nature of the book chaptersit is not possible to organize the book into thematic areas and each chapter should betreated independently of the others. a brief account of each chapter is given nextIn Chapter 1, Mattingley and Boyd elaborate on the concept of convex optimizationin real-time embedded systems and automatic code generation. As opposed to genericsolvers that work for general classes of problems, in real-time embedded optimization thesame optimization problem is solved many times, with different data, often with a hardreal-time deadline. Within this setup the authors propose an automatic code-generationsystem that can then be compiled to yield an extremely efficient custom solver for theproblem familyIn Chapter 2, Beck and Teboulle provide a unified view of gradient-based algorithmsfor possibly nonconvex and non-differentiable problems, with applications to signalrecovery. They start by rederiving the gradient method from several different perspectives and suggest a modification that overcomes the slow convergence of the algorithmThey then apply the developed framework to different image-processing problems suchas e1-based regularization, TV-based denoising, and Tv-based deblurring, as well ascommunication applications like source localizationIn Chapter 3, Songsiri, Dahl, and Vandenberghe consider graphical models for autore-gressive processes. They take a parametric approach for maximum-likelihood andmaximum-entropy estimation of autoregressive models with conditional independenceconstraints, which translates into a sparsity pattern on the inverse of the spectral-densitymatrix. These constraints turn out to be nonconvex. To treat them the authors proposea relaxation which in some cases is an exact reformulation of the original problem. Theproposed methodology allows the selection of graphical models by fitting autoregressiveprocesses to different topologies and is illustrated in different applicationsThe following three chapters deal with optimization problems closely related to SDPand relaxation techniquesIn Chapter 4, Luo and Chang consider the SDP relaxation for several classes ofquadratic-optimization problems such as separable quadratically constrained quadraticprograms(QCQPs)and fractional QCQPs, with applications in communications and signal processing. They identify cases for which the relaxation is tight as well as classes ofquadratic-optimization problems whose relaxation provides a guaranteed, finite worstcase approximation performance. Numerical simulations are carried out to assess theefficacy of the SDP-relaxation approach
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