Non-Ergodic Dynamics of the 2D Random-phase Sine-Gordon Model Applications to Vortex-Glass
随机激励的非线性Markov跳变系统的稳态响应

随机激励的非线性Markov跳变系统的稳态响应宦荣华;马云双;郝琪;朱位秋【摘要】大量实际工程问题需要用同时包含连续和离散变量的Markov跳变系统来描述.本文介绍了一类随机激励的单自由度(强)非线性Markov跳变系统的稳态响应的研究方法.首先,基于随机平均法导出具有Markov跳变参数的平均It(O)随机微分方程,原系统方程的维数得到降低.接着,根据跳变过程原理,建立Fok-ker-Planck-Kolmogorov (FPK)方程组,方程组中的方程与系统的结构状态一一对应且互相耦合.求解该FPK方程组,得到Markov跳变系统的稳态随机响应及其统计量.最后,以一个高斯白噪声激励的Markov跳变Duffing振子为例,计算得到不同跳变规律下系统的稳态响应.研究结果表明,Markov跳变系统的稳态响应可以看作是各结构状态子系统稳态响应的加权和,加权值由跳变规律决定.【期刊名称】《动力学与控制学报》【年(卷),期】2016(014)002【总页数】5页(P165-169)【关键词】Markov跳变;随机激励;非线性;随机平均法【作者】宦荣华;马云双;郝琪;朱位秋【作者单位】浙江大学应用力学研究所,杭州310027;中国南车青岛四方机车车辆股份有限公司,青岛266111;浙江大学应用力学研究所,杭州310027;浙江大学应用力学研究所,杭州310027【正文语种】中文2014-11-01收到第1稿,2015-04-01收到修改稿.*国家自然科学基金资助项目(11372271,11432012,51175474)、“973”计划(2011CB711105)随着计算机、军事、生物和工业技术的发展,经典的单结构系统理论已不能满足实际应用的需要,而一类既能反映系统状态变化又能反映系统结构变化的系统,即Markov跳变系统,从20世纪中叶提出以来引起人们的很大关注. Markov跳变系统是一个同时包含连续和离散变量的混合系统,离散跳变随机过程的引入,使得系统的动力学行为更为复杂,也增加了系统动力学研究的难度.因此,Markov跳变系统动力学的研究具有重要科学意义.Markov跳变系统最初由Krasivskii和Lidskii提出[1],经过几十年的发展,已经取得了一些成果[2 -3]. Markov跳变系统的稳定性理论是由Kats 和Krasovskii最先提出的[4].随后,M. martion[5]利用随机Lyapunov方法分析了随机噪声环境下线性Markov跳变系统的均方稳定性. Fen和Fang[6,7]将传统的Lyapunov稳定性理论拓展到随机Markov跳变系统中,提出了随机Lyapunov第二方法稳定性定理. Krasovskii等首先研究了Markov跳变系统的LQR问题. Sworder等[8]基于极大值原理研究了有限时间区间内的线性Markov跳跃系统的最优控制问题. Ghosh等[9]提出了Markov跳变系统控制问题的动态规划方法.方洋旺[3]等对近20年里面随机跳变系统在状态估计、稳定性研究以及最优控制方面的主要理论进行了总结.然而,之前的研究多为线性系统,研究内容多局限于随机稳定性与控制方面,对非线性Markov跳变系统的动力学研究还极少涉及.本文主要研究了随机激励下非线性Markvo跳变系统的稳态响应.基于随机平均法[10 -13]对系统进行简化,导出了Markov跳变系统的平均Itô随机微分方程.建立并求解相应的FPK方程组,得到Markov跳变系统稳态振幅响应的概率分布,研究了跳变规律对系统稳态响应的影响规律.将理论结果与数值仿真结果进行对比,验证了本文理论方法的准确性.考虑一类随机激励的单自由度(强)非线性Markov跳变系统:式中ε为小量,g为非线性刚度;εf为带有跳变参数的小阻尼;ε1/2hW(t)代表带有跳变参数的弱外或参数激励;W(t)为强度为2D的高斯白噪声.当s (t)固定时,函数f(x,˙x,s(t))和h(x,˙x,s(t))为x的光滑函数. s(t)是一个在有限集合S ={1,2,…,l}内取值的连续时间离散状态的Markov过程,s(t)代表系统结构的状态标号,l是系统所拥有的结构状态的数目.在小时间间隔Δt内,该Markov过程的转移概率为式中为跳变率矩阵表示从时间t时刻系统结构状态为i到t +Δt时刻系统结构状态为j的跳变率,满足考虑独立跳变情形,即跳变过程与系统状态无关.假设系统始终运行在第i个结构状态中,无跳变发生.在此情形下,简单起见,令f(x,s(t))和h(x,s(t))简写为f(i)(x,˙x)和h(i)(x˙).无跳变系统具有如下形式的解[10]式中其中A,Φ,τ和υ为随机过程.运用随机平均法[10 -12],得到关于振幅响应A的平均Itô随机微分方程式中B(t)为单位维纳过程,扩散和漂移系数为表示对Φ从0到2π的平均.原跳变系统具有l个如式(6)所示的平均方程,平均后的跳变系统在这l个平均方程间跳变.因此,得到如下跳变系统的平均方程式中m(A,s)和σ(A,s)为带有Markov跳变参数的扩散和漂移系数,当系统运行在第i个结构状态时,其扩散和漂移系数,即m(A,s = i)和σ(A,s = i)由方程(7)确定.假设在很小的时间区间Δt内系统未发生跳变,则转移概率密度函数p(A,s,t|A′,s,t′)满足如下FPK方程:当在Δt内,系统发生了跳变,则此时概率密度函数p(A,s,t +Δt)为式中条件概率密度q(A,s,t +Δt|A′,r,t)表示t时刻和r状态的振幅响应A′为已知的条件下,振幅A 在t +Δt时刻和s状态的概率分布. q(A,s,t +Δt|A′,r,t)的具体形式由实际问题的物理意义所决定.当跳变参数与系统状态无关时,即为独立跳变时,q(A,s,t +Δt|A′,r,t)具有如下形式将方程(2)和(9)代入方程(10),并令Δt→0,方程(10)变为方程(12)即为混合随机过程[A,s]T联合概率密度p(A,s,t)所满足的FPK方程.对于独立跳变过程,利用式(11),FPK方程(12)可以简化为初始条件边界条件式(13)是由l个方程组成的方程组(l为系统所包含的所有结构状态数目),且这些方程通过零次方项耦合. FPK方程(13)一般难以求解,即使是数值解也难以获得.若仅考虑稳态解,即令∂p/∂t =0,则方程(13)可以得到简化.通过差分法等数值方法求解简化后的FPK方程,可得到稳态联合概率密度p(A,s).则振幅的概率密度p(A)为式中c为归一化常数.考虑一个随机激励的跳变Duffing振子,其运动微分方程为式中β(s)为跳变阻尼系数;h(s)为跳变外激励系数;W(t)为强度为2D的高斯白噪声;s为连续时间的Markov随机过程,其转移概率如式(2)所示.本文考虑2结构状态情形,即有限集合S ={1,2}.利用上述随机平均法,得到如(8)式所示的平均方程,跳变扩散和漂移系数为建立和求解简化后的FPK方程可得到跳变系统的振幅响应的稳态概率密度p(A).假设系统无量纲参数为:ω=1. 0,α=1. 0,D = 0. 1,β(s =1)=0. 1,β(s =2)=0. 2,h(s =1)=2. 0,h(s =2)=1. 0.图1为不同跳变规律下跳变系统振幅响应的稳态概率密度.图1(a)中Λ1和Λ3所代表曲线分别为系统结构状态为s =1和s =2时的无跳变系统的稳态振幅概率密度,Λ2为跳变系统的稳态振幅概率密度,结果表明,发生跳变后系统的稳态响应相比于无跳变系统发生非常大的变化,跳变对系统的响应具有很大影响.图1(a)中Λ2为对称矩阵,所对应的跳变系统为对称跳变.图1(b)为系统发生非对称跳变时的振幅稳态概率密度.当Λ =Λ4时,系统从结构状态s =1跳变到s =2的概率比跳回结构状态s =1的概率要小,即系统停留在s =1结构状态的概率较大,因此,图1(b)中Λ4代表的曲线更接近于曲线Λ1,而Λ5则更接近Λ3.显然,跳变系统的响应可以看作是各结构状态下无跳变系统响应的加权和,而加权值由跳变规律决定.不同跳变规律下系统的响应具有很大差异.图1中实线为解析结果,符号曲线代表数值仿真结果,两者吻合,表明了本文提出的理论方法的准确性.图2和3分别为振幅响应A和Markov跳变参数s(t)的一段样本.本文提出了一种研究随机激励下Markov跳变非线性系统的稳态响应的求解方法.本文的主要工作是导出了非线性Markov跳变系统的平均Itô随机微分方程,得到了相应的FPK方程组.随机平均法的应用,降低了系统方程的维数,使得最后的FPK方程组的数值求解成为可能. Markov跳变Duffing振子算例的计算验证了本文提出的理论方法的有效性.本文虽然只对单自由度Markov跳变系统的响应进行了研究,但该理论方法在多自由度Markov跳变系统的响应、稳定性与可靠性等的研究方面也具有很大潜力.【相关文献】1 Krasosvkii N N,Lidskii E A. Analytical design of controllers in systems with random attributes. Automation and Remote Control,1961,22(1):1021~10252 吴森堂.结构随机跳变系统理论及其应用.北京:科学出版社,2007(Wu S T. The theory of stochastic jump system and its application. Beijing:Science Press,2007(in Chinese))3 方洋旺,伍友利,王洪强.结构随机跳变系统最优控制理论.北京:国防工业出版社,2012(Fang Y W,Wu Y L,Wang H Q. Optimal control theory of stochastic jump sytem. Beijing:National Defense Industry Press. 2012(in Chinese))4 Kats L,Krasovskii N. On the stability of systems with random parameters. Journal of Applied Mathematics and Mechanics,1960,27(5):809~8235 Mariton M. Almost sure and moment stability of jump linear systems. Systems & Control Letters,1988,11(5):393~3976 Feng X,Loparo KA,Ji Y. et al. Stochastic stability properties of jump linear systems. IEEE Trans Automate Control,1992,37(1):38~457 Fang Y G. A new general sufficient condition for almost sure stability of jump linear systems. IEEE Transaction Automate Control,1997,42(3):378~3828 Sworder D. Feedback control of a class of linear systems with jump parameters. IEEE Trans on Automatic Control,1969,14(1):9~149 Ghosh M K,Arapostathis A,Marcus S I. Ergodic control of switching diffusions. SIAM Journal on Control and Optimization,1997,35(6):152~19810 Xu Z,Chung Y K. Averaging method using generalized harmonic functions for strongly nonlinear oscillators. Journal of Sound and Vibration,1994,174(4):563~57611 Huang Z L,Zhu W Q,Suzuki Y. Stochastic averaging of strongly nonlinear oscillators under combined harmonic and white noise excitations. Journal of Sound and Vibration,2000,238(2):233~25612 Huang Z L,Zhu W Q. Stochastic averaging of quasi-integrable hamiltonian systems under combined harmonic and white noise excitations. International Journal of Non-Linear Mechanics,2004,39(9):1421~143413 陈林聪,朱位秋.随机扰动下简单电力系统的可靠度反馈最大化.动力学与控制学报,2010,8(1):19~23 (Chen L C,Zhu W Q. Feedback maximization of reliability of a simple power system under random perturbations. Journal of Dynamics and Control,2010,8(1):19~23(in Chinese))Received 1 November 2014,revised 1 April 2015.*This project supported by the National Natural Science Foundation of China(11372271,11432012,51175474);“973”program(2011CB711105)。
自旋玻璃与消息传递算法Spin Glass and Message-Passing 概要

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thevertexcoverproblem38alwaysuncoveredalwayscoveredunfrozenweigthartmannprl2000pre200139newvertexuncoverednewvertexpartiallycoverednewvertexalwayscovered40meanfieldtheoryresultislowerthanexperimentalvaluesforce271832718341假定的相空间结构42引入参数y43neighborsvertexiprobabilityvcsizeincreasereweightedprobabilityallunfrozenoralwayscoveredalwaysuncovered0atleastonealwaysuncoveredunfrozenoralwayscovered1444546同样的消息传递的算法可以用于解决神经网络?信息系统?满足性问题?
清华考博辅导:清华大学计算机科学与技术考博难度解析及经验分享

清华考博辅导:清华大学计算机科学与技术考博难度解析及经验分享根据教育部学位与研究生教育发展中心最新公布的第四轮学科评估结果可知,全国共有168所开设计算机科学与技术专业的大学参与了2017-2018计算机科学与技术专业大学排名,其中排名第一的是北京大学,排名第二的是清华大学,排名第三的是浙江大学。
作为清华大学实施国家“211工程”和“985工程”的重点学科,计算机科学与技术一级学科在历次全国学科评估中均名列第二。
下面是启道考博整理的关于清华大学计算机科学与技术考博相关内容。
一、专业介绍计算机科学与技术是研究计算机的设计与制造,并利用计算机进行有关的信息表示、收发、存储、处理、控制等的理论方法和技术的学科。
计算机专业涵盖计算机科学与技术、计算机软件工程、计算机信息工程等专业,主要培养具有良好的科学素养,系统地、较好地掌握计算机科学与技术,包括计算机硬件和软件组成原理、计算机操作系统、计算机网络基础、算法与数据结构等,计算机的基本知识和基本技能与方法,能在科研部门、教育、企业、事业、行政管理部门等单位从事计算机教学、科学研究和计算机科学与技术学科的应用。
清华大学计算机科学与技术专业在博士招生方面,划分为3个研究方向:081200计算机科学与技术研究方向:01信息安全;02机器智能;03金融科技;04网络科学;05计算生物学;06能源信息科学;07机器人;08理论计算机科学;09量子信息此专业实行申请考核制。
二、考试内容清华大学计算机科学与技术专业博士研究生招生为资格审查加综合考核形式,由笔试+面试构成。
其中,综合考核内容为:综合考核形式为面试:每位考生约30 分钟,满分100 分。
面试重点考查申请人在本学科攻读博士学位的基本素养、学术能力、学术志趣等。
三、时间安排1.博士生申请在每年的8-9月和11月。
2.直博生(包括夏令营拟录取的直博生)、硕博连读生及部分9月份招收普博生的院系8-9月申请,9月中下旬考试录取,见当年招生简章及目录、招生说明、直博直硕招生要求。
实验二:分子与表面的对接优化及分子动力学模拟-副本

介绍我的手机作文介绍我的手机作文(精选26篇)在日常学习、工作和生活中,大家总少不了接触作文吧,写作文可以锻炼我们的独处习惯,让自己的心静下来,思考自己未来的方向。
怎么写作文才能避免踩雷呢?下面是小编为大家收集的介绍我的手机作文(精选26篇),希望能够帮助到大家。
介绍我的手机作文篇1“好漂亮的手机哦!在哪里买的?”我的一伙同学正在参观我的新手机呢!五分钟后拜拜,我们下次还来哦!呼终于把她们打发走了。
现在就给你介绍一下我的新手机吧!它的外壳是玫瑰红的,外圈有一层白色,手机的功能一应俱全,壁纸是我的一张照片,是在去年儿童节的时候拍的。
按左键菜单就出来了,中间是写信息,信息分两大类:一种是短信,另外一种是彩信,我的手机发短信非常容易的,因为是触摸屏嘛!直接用手写就行了。
有时候我奶奶要给我叔叔发短信我的手机可派到用处了。
短信的东北方是娱乐和游戏,里面的游戏太多了我都玩不过来了,里面有:雷霆战机、丛林冒险、合金弹头、QQ跑车、梦幻麻将、天空合金、中国象棋、君临天下、三国麻将,俄罗斯方块、终结者、宇宙骑士,全部在里面了。
多不多呀?新手机还有音乐播放器,音乐播放器里面的歌曲有:爱情买卖、花蝴蝶、舞娘、日不落、七上八下、美人计﹑波斯猫都是我下载的哇!里面连电子书都有呀!短信的左边是影音天地,里面的菜单有:相机、视频播放器、录音、调频广播、相册,相册里面有好多相片,里面有去汝水公园的相片,去王安石纪念馆的相片,还有一些是我从书上拍下来的,看完了相片来看一部电影吧!电影有:猫和老鼠与福尔摩斯﹑百变小樱停电的时候我就把手机当电视看。
电话簿上记载着我的亲戚和同学们的电话。
有了手机我不会因为没有人叫我起床而迟到,我叫妈妈说一句早上叫我起床的话录下来,设为闹钟铃声。
了早上,它就像妈妈在我身边叫我一样,叫我起床。
多亲切啊!现在我和我的手机已经成为了一对形影不离的好朋友。
介绍我的手机作文篇2我有一部手机,那是我妈妈在我过生日的时候为我买的,这是我最喜欢,也是最珍贵的东西。
数学类SCI分区查询

SIAM REV SIAM REVIEWJ AM MATH SOC JOURNAL OF THE AMERICAN MATHEMATICAL SOCIETYANN MATH ANNALS OF MATHEMATICSB AM MATH SOC BULLETIN OF THE AMERICAN MATHEMATICAL SOCIETYJ R STAT SOC B JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATIS J AM STAT ASSOC JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION STRUCT EQU MODELING STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL MULTIVAR BEHAV RES MULTIVARIATE BEHAVIORAL RESEARCHINT J INFECT DIS DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SE COMMUN PUR APPL MATH COMMUNICATIONS ON PURE AND APPLIED MATHEMATICSRISK ANAL RISK ANALYSISANN STAT ANNALS OF STATISTICSSIAM J SCI COMPUT SIAM JOURNAL ON SCIENTIFIC COMPUTINGMATH MOD METH APPL S MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCESSIAM J MATRIX ANAL A SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS MULTISCALE MODEL SIM MULTISCALE MODELING & SIMULATIONINVENT MATH INVENTIONES MATHEMATICAEJ R STAT SOC A STAT JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATIS STAT SCI 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5.3326672922245921782519.66667 0894-0347数学 2.552 2.3 2.581 2.4853331457123011041263.66667 0003-486X数学 2.4262 1.845 2.0933336285529654555678.66667 0273-0979数学 2.385 1.8 2.962 2.3823332304194919862079.66667 1369-7412数学 2.3152 2.691 2.3223337168629556426368.33333 0162-1459数学 2.171 1.7 1.978 1.95314510131941272513476.3333 1070-5511数学 2.143 1.2 1.919 1.7693332549209317812141 0027-3171数学 2.095 1.20.952 1.4033331394124610551231.66667 1201-9712数学 2.0620.20.0860.7943335114920193.333333 0010-3640数学 2.031 1.8 1.694 1.8553334407390038584055 0272-4332数学 1.938 1.5 1.321 1.5896672521204419772180.66667 0090-5364数学 1.902 1.7 1.625 1.7347253631061186560.33333 1064-8275数学 1.824 1.5 1.231 1.5213334360367731623733 0218-2025数学 1.805 1.2 1.31 1.454333894768674778.666667 0895-4798数学 1.798 1.10.727 1.2243331658149711341429.66667 1540-3459数学 1.723 1.7 1.135 1.52966727814955160.666667 0020-9910数学 1.659 1.7 1.926 1.7456675025443846424701.66667 0964-1998数学 1.547 1.10.796 1.1393331296119310991196 0883-4237数学 1.531 1.8 1.423 1.6011599139712301408.66667 1435-9855数学 1.486 1.40.95 1.28333318311888129.666667 0036-1399数学 1.425 1.1 1.189 1.2446673682339732123430.33333 0012-7094数学 1.409 1.3 1.118 1.2773147278427622897.66667 0073-8301数学 1.353 1.2 1.529 1.354667760690809753 0036-1429数学 1.335 1.4 1.106 1.2776675308439936234443.33333 0001-5962数学 1.333 1.8 2.2 1.7703332103193419451994 0266-5611数学 1.319 1.5 1.344 1.4013332264208417242024 0960-3174数学 1.3050.80.7610.938667675530484563 0091-1798数学 1.301 1.1 1.189 1.2073332521222424382394.33333 0294-1449数学 1.29210.753 1.024873795718795.333333 0885-7474数学 1.281 1.70.978672543405 1364-0380数学 1.274 1.30.849667386236207.333333 1615-3375数学 1.2690.9 1.5 1.216333127826491 0363-0129数学 1.263 1.2 1.048 1.1553548306026333080.33333 1063-5203数学 1.226 1.4 1.456 1.354741603581641.666667 1050-5164数学 1.211 1.4 1.37 1.317108810709661041.33333 1052-6234数学 1.211 1.2 1.213 1.2206671816166413091596.33333 0075-8434数学 1.2060.40273042434.66667 1468-1218数学 1.1940.70.4770.77666722311777139 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Entropy

Department of Mathematics Oregon State University Corvallis, OR 97331 U.S.A. burton@
1
Abstract
We consider the entropy of systems of random transformations, where the transformations are chosen from the set of generators of a Zd action. We show that the classical de nition gives unsatisfactory entropy results in the higher-dimensional case, i.e. when d 2. We propose a new de nition of the entropy for random group actions which agrees with the classical de nition in the one-dimensional case, and which gives satisfactory results in higher dimensions. We identify the entropy by a concrete formula which makes it possible to compute the entropy in certain cases. Along the way, we show that the random version of Krieger's theorem on the existence of nite generators is not valid.
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ERGODIC THEOREMS FOR QUANTUM KRONECKER FLOWS
2
S. KLIMEK AND A. LESNIEWSKI
Kronecker ow. Consider the following dynamics on a quantum torus, namely the quantum Kronecker ow. Recall that the algebra A of observables on a quantum
< a >= (a)I + Ca ;
( n; a n) = ( n; < a > n) = (a) + ( n ; Ca n ) ! (a); as n ! 1 :
where Ca is compact. If this is the case, then we have (1.2.2)
In physics, H is the Hamiltonian of a system, and the limit n ! 1 is the semiclassical limit. For this reason, we refer to the above type of ergodic behavior as semiclassical ergodicity. It is very di cult to establish (1.2.1) in examples of quantum dynamical systems. A weaker version of (1.2.1) has been studied extensively in the literature, see Z] and references therein. The starting point is equation (1.2.2), (a) = nlim ( n ; a n ): !1
A new test for chaos justification
A new test for chaosin deterministic systemsBy Georg A.Gottwald†and Ian Melbourne‡We describe a new test for determining whether a given deterministic dynamical system is chaotic or nonchaotic.In contrast to the usual method of computing the maximal Lyapunov exponent,our method is applied directly to the time series data and does not require phase space reconstruction.Moreover,the dimension of the dynamical system and the form of the underlying equations is irrelevant.The input is the time series data and the output is0or1depending on whether the dynamics is non-chaotic or chaotic.The test is universally applicable to any deterministic dynamical system,in particular to ordinary and partial differential equations,and to maps.Our diagnostic is the real valued function p(t)= t0φ(x(s))cos(θ(s))d s whereφis an observable on the underlying dynamics x(t)andθ(t)=ct+ t0φ(x(s))d s.The constant c>0isfixed arbitrarily.We define the mean-square-displacement M(t) for p(t)and set K=lim t→∞log M(t)/log ing recent developments in ergodic theory,we argue that typically K=0signifying nonchaotic dynamics or K=1 signifying chaotic dynamics.Keywords:Chaos,deterministic dynamical systems,Lyapunov exponents,mean square displacement,Euclidean extension1.IntroductionThe usual test of whether a deterministic dynamical system is chaotic or nonchaotic is the calculation of the largest Lyapunov exponentλ.A positive largest Lyapunov exponent indicates chaos:ifλ>0,then nearby trajectories separate exponentially and ifλ<0,then nearby trajectories stay close to each other.This approach has been widely used for dynamical systems whose equations are known(Abarbanel et al.1993;Eckmann et al.1986;Parker&Chua1989).If the equations are not known or one wishes to examine experimental data,this approach is not directly applicable. However Lyapunov exponents may be estimated(Wolf et al.1985;Sana&Sawada 1985;Eckmann et al.1986;Abarbanel et al.1993)by using the embedding theory of Takens(1981)or by approximating the linearisation of the evolution operator. Nevertheless,the computation of Lyapunov exponents is greatly facilitated if the underlying equations are known and are low-dimensional.In this article,we propose a new0–1test for chaos which does not rely on knowing the underlying equations,and for which the dimension of the equations is irrelevant.The input is the time series data and the output is either a0or a1 depending on whether the dynamics is nonchaotic or chaotic.Since our method is †School of Mathematics and Statistics,University of Sydney,NSW2006,Australia‡Department of Mathematics and Statistics,University of Surrey,Guildford,Surrey GU27XH, UKArticle submitted to Royal Society T E X Paper2G.Gottwald and I.Melbourneapplied directly to the time series data,the only difference in difficulty between analysing a system of partial differential equations or a low-dimensional system of ordinary differential equations is the effort required to generate sufficient data.(As with all approaches,our method is impracticable if there are extremely long tran-sients or once the dimension of the attractor becomes too large.)With experimental data,there is the additional effect of noise to be taken into consideration.We briefly discuss this important issue at at the end of this paper.However,our aim in this paper is to present ourfindings in the situation of noise-free deterministic data.2.Description of the0–1test for chaosTo describe the new test for chaos,we concentrate on the continuous time case and denote a solution of the underlying system by x(t).The discrete time case is handled analogously with the obvious modifications.Consider an observableφ(x) of the underlying dynamics.The method is essentially independent of the actual form ofφ—almost any choice ofφwill suffice.For example if x=(x1,x2,...,x n) thenφ(x)=x1is a possible and simple choice.Choose c>0arbitrarily and defineθ(t)=ct+ t0φ(x(s))d s,p(t)= t0φ(x(s))cos(θ(s))d s.(2.1)(Throughout the examples in§3and§4wefix c=1.7once and for all.)We claim that(i)p(t)is bounded if the underlying dynamics is nonchaotic and(ii)p(t)behaves asymptotically like a Brownian motion if the underlying dynam-ics is chaotic.The definition of p in(2.1),which involves only the observableφ(x),highlights the universality of the test.The origin and nature of the data which is fed into the system(2.1)is irrelevant for the test,and so is the dimension of the underlying dynamics.Later on,we briefly explain the justification behind the claims(i)and(ii).For the moment,we suppose that the claims are correct and show how to proceed.To characterise the growth of the function p(t)defined in(2.1),it is natural to look at the mean square displacement(MSD)of p(t),defined to be1M(t)=limT→∞A new test for chaos3 which obviously does not change the slope K.)This allows for a clear distinction of a nonchaotic and a chaotic system as K may only take values K=0or K=1. We have lost though the possibility of quantifying the chaos by the magnitude of the largest Lyapunov exponentλ.Numerically one has to make sure that initial transients have died out so that the trajectories are on(or close to)the attractor at time zero,and that the integration time T is long enough to ensure T t.3.An example:the forced van der Pol oscillatorWe illustrate the0–1test for chaos with the help of a concrete example,the forced van der Pol system,˙x1=x2˙x2=−d(x21−1)x2−x1+a cosωt(3.1) which has been widely studied in nonlinear dynamics(van der Pol1927;Guck-enheimer&Holmes1990).Forfixed a and d,the dynamics may be chaotic or nonchaotic depending on the parameterω.Following Parlitz&Lauterborn(1987), we take a=d=5and letωvary from2.457to2.466in increments of0.00001. Chooseφ(x)=x1+x2and c=1.7.We stress that the results are independent of these choices for all practical purposes.As described below in§5,almost all choices will work.(Deliberately poor choices such as c=0,orφ=7orφ=t obviously fail;sensible choices that fail are virtually impossible tofind.)Infigure1we show a plot of K versusω.The periodic windows are clearly seen. As a comparison we show infigure2the largest Lyapunov exponentλversusω. Since the onset of chaos does not occur until afterω=2.462we display the results only for the range2.462<ω<2.466infigures1and2.(Both methods easily indicate regular dynamics for2.457<ω<2.462.)Naturally we do not obtain the values K=0and K=1exactly–the mathe-matical results that underpin our method predict these values in the limit of infi-nite integration time.(The same caveat applies equally to the Lyapunov exponent method.)In producing the data forfigures1and2,we allowed for a transient of 200,000units of time and then integrated up to time T=2,000,000.As can be seen infigure3,for most of the400data points in the range ofω,we obtain K>0.8or K<0.01.Next,we carry out the0–1test for the forced van der Pol system in the situation of a more limited quantity of data.The results are shown infigure4for2.463<ω<2.465.We again allow for a transient time200,000but then integrate only for T=50,000.The transitions between chaotic dynamics and periodic windows are almost as clear with T=50,000as they are with T=2,000,000even though the convergence of K to0or1is better with T=2,000,000.Article submitted to Royal Society4G.Gottwald and I.Melbourne 00.20.40.60.811.22.462 2.463 2.464 2.465 2.466Figure 1.Asymptotic growth rate K of the mean square displacement (2.2)versus ωfor the van der Pol system (3.1)determined by a numerical simulation of the skew product system (3.1)and (2.1)with a =d =5,c =1.7,φ(x )=x 1+x 2and ωvarying from 2.462to 2.466.The integration interval is T =2,000,000after an initial transient of 200,000units of time.-0.1-0.08-0.06-0.04-0.020.020.040.060.080.12.462 2.463 2.464 2.465 2.466Figure rgest Lyapunov exponent λversus ωfor the van der Pol system (3.1)with a =d =5and ωvarying from 2.462to 2.466(cf Parlitz &Lauterborn 1987).The integration interval is T =2,000,000after an initial transient of 200,000units of time.Article submitted to Royal SocietyA new test for chaos 500.20.40.60.811.22.462 2.463 2.464 2.465 2.466Figure 3.Asymptotic growth rate K versus ωfor the van der Pol system (3.1)as in figure 1with T =2,000,000.The horizontal lines represent K =0.01and K =0.8.0.20.40.60.811.22.463 2.464 2.46500.20.40.60.811.22.463 2.464 2.465Figure 4.Asymptotic growth rate K versus ωvarying from 2.463to 2.465for the van der Pol system as in figures 1and 3but with integration interval T =50,000after an initial transient of 200,000units of time.The horizontal lines represent K =0.01and K =0.8.Article submitted to Royal Society6G.Gottwald and I.Melbourne4.Further examplesTo test the method on high-dimensional systems we investigated the driven and damped Kortweg-de Vries(KdV)equation(Kawahara&Toh1988)u t+uu x+βu xxx+αu xx+νu xxxx=0,(4.1) on the interval[0,40]with periodic boundary conditions.This partial differential equation has non-chaotic solutions if the dispersionβis large and exhibits spatio-temporal chaos for sufficiently smallβ.Note that equation(4.1)reduces to the KdV equation whenα=ν=0,and reduces to the Kuramoto-Sivashinksy equation whenβ=0.Wefixα=2,ν=0.1and varyβ.Forβ=0,it is expected that the dynamics of the Kuramoto-Sivashinksy equation are chaotic for these parameter values.As an observable we usedφ(u(x,t))=u(x0,t)where x0is an arbitrarilyfixed position, and we iterated until time T=35,000.The0–1test confirms that the dynamics is chaotic atβ=0(with K=0.939).Also,we obtain K=0.989atβ=0.1and K=0.034atβ=4,indicating chaotic and regular dynamics respectively at these two parameter values.Finally,for discrete dynamical systems,we tried out the test with an ecological model whose chaotic component is coupled with strong periodicity(Brahona& Poon1996;Cazelles&Ferriere1992).The modelx k+1=118y k exp(−0.001(x k+y k))y k+1=0.2x k exp(−0.07(x k+y k))+0.8y k exp(−0.05(0.5x k+y k))has a non-connected chaotic attractor consisting of seven connected components. Our test yields K=1.023with only10,000data points and clearly shows that the dynamics is chaotic.5.Justification of the0–1test for chaosThe function p(t)can be viewed as a component of the solution to the skew product system˙θ=c+φ(x(t))˙p=φ(x(t))cosθ(5.1)˙q=φ(x(t))sinθdriven by the dynamics of the observableφ(x(t)).Here(θ,p,q)represent coordinates on the Euclidean group E(2)of rotationsθand translations(p,q)in the plane.We note that inspection of the dynamics of the(p,q)-trajectories of the group extension provides very quickly(for small T)a simple visual test of whether the underlying dynamics is chaotic or nonchaotic as can be seen fromfigure5.Article submitted to Royal SocietyA new test for chaos7(a)(b)Figure5.The dynamics of the translation components(p,q)of the E(2)-extension(5.1) for the van der Pol system(3.1)with a=d=5,c=1.7,φ(x)=x1+x2.These plots were obtained by integrating for T∼1400(with timestep0.01).In(a),an unbounded trajectory is shown corresponding to chaotic dynamics atω=2.46550.In(b),a bounded trajectory is shown corresponding to regular dynamics atω=2.46551.Article submitted to Royal Society8G.Gottwald and I.MelbourneIn Nicol et al.(2001)it has been shown that typically the dynamics on the group extension is sublinear and furthermore that typically the dynamics is bounded if the underlying dynamics is nonchaotic and unbounded(but sublinear)if the underlying dynamics is chaotic.Moreover,the p and q components each behave like a Brownian motion on the line if the chaotic attractor is uniformly hyperbolic(Field et al. 2002).A nondegeneracy result of Nicol et al.(2001)ensures that the variance of the Brownian motion is nonzero for almost all choices of observableφ.Recent work of Melbourne&Nicol(2002)indicates that these statements remain valid for large classes of nonuniformly hyperbolic systems,such as H´e non-like attractors.There is a weaker condition on the‘chaoticity’of X that guarantees the desired growth rate K=1for the mean square displacement(2.2):namely that the autocor-relation function ofφ(x)cos(θ)decays at a rate that is better than quadratic.More precisely,let x(t)andθ(t)denote solutions to the skew product equations(5.1)with initial conditions x0andθ0respectively.If there are constants C>0andα>2 such thatφ(x(t))cos(θ(t))φ(x0)cosθ0d x0dθ0 ≤Ct−α,for all t>0,then K=1as desired(Biktashev&Holden1998;Ashwin et al.2001; Field et al.2002).(Again,results of Nicol et al.(2001);Field et al.(2002);and Melbourne&Nicol(2002)ensure that the appropriate nondegeneracy condition holds for almost all choices ofφ.)There is a vast literature on proving decay of correlations(Baladi1999)and this has been generalised to the equivariant setting for discrete time by Field et al.(2002)and Melbourne&Nicol(2002)and for continuous time by Melbourne&T¨o r¨o k(2002).It follows from these references that K=1,for large classes of chaotic dynamical systems.One might ask why it is not better to work,instead of the E(2)-extension,with the simpler R-extension˙p=φ(x(t).In principle,p(t)can again be used as a detector for chaos.However,by the ergodic theorem p(t)will typically grow linearly with rate equal to the space average of φ.This would lead to K=2irrespective of whether the dynamics is regular or chaotic.Hence,it is necessary to subtract offthe linear term of p(t)in order to observe the bounded/diffusive growth that distinguishes between regular/chaotic dynamics.Subtracting this linear term is a highly nontrivial numerical obstruc-tion.The inclusion of theθvariable in the definition(2.1)of p(t)and in the skew product(5.1)kills offthe linear term.6.DiscussionWe have established a simple,inexpensive,and novel0–1test for chaos.The com-putational effort is of low cost,both in terms of programming efforts and in terms of actual computation time.The test is a binary test in the sense that it can only distinguish between nonchaotic and chaotic dynamics.This distinction is extremely clear by means of the diagnostic variable K which has values either close to0or close to1.The most powerful aspect of our method is that it is independent of the nature of the vectorfield(or data)under consideration.In particular the equationsArticle submitted to Royal SocietyA new test for chaos9 of the underlying dynamical system do not need to be known,and there is no prac-tical restriction on the dimension of the underlying vectorfield.In addition,our method applies to the observableφ(x(t))rather than the full trajectory x(t).Related ideas(though not with the aim to detect chaos)have been used for PDE’s in the context of demonstrating hypermeander of spirals in excitable me-dia(Biktashev&Holden1998)where the spiral tip appears to undergo a planar Brownian motion(see also Ashwin et al.2001).We note also the work of Coullet &Emilsson(1996)who studied the dynamics of Ising walls on a line,where the motion is the superposition of a linear drift and Brownian motion.(This is an ex-ample of an R-extension which we mentioned briefly in§5.As we pointed out then, the linear drift is an obstruction to using an R-extension to detect chaos.) From a purely computational point of view,the method presented here has a number of advantages over the conventional methods using Lyapunov exponents. At a more technical level,we note that the computation of Lyapunov exponents can be thought of abstractly as the study of the GL(n)-extension˙A=(d f)Ax(t)where GL(n)is the space of n×n matrices,A∈GL(n),and n is the size of the system.Note that the extension involves n2additional equations and is defined us-ing the linearisation of the dynamical system.To compute the dominant exponent, it is still necessary to add n additional equations,again governed by the linearised system.In contrast,our method requires the addition of two equations.In this paper,we have concentrated primarily on the idealised situation where there is an in principle unlimited amount of noise-free data.However,in§3we also demonstrated the effectiveness of our method for limited data sets.An issue which will be pursued in further work is the effect of noise which is inevitably present in all experimental time series.Preliminary results show that our test can cope with small amounts of noise without difficulty.A careful study of this capability,and comparison with other methods,is presently in progress.We are grateful to Philip Aston,Charlie Macaskill and Trevor Sweeting for helpful dis-cussions and suggestions.The research of GG was supported in part by the European Commission funding for the Research Training Network“Mechanics and Symmetry in Europe”(MASIE).ReferencesAbarbanel H.D.I.,Brown R.,Sidorovich J.J.&Tsimring L.S.1993The analysis of observed chaotic data in physical systems.Rev.Mod.Phys.65,1331–1392.Ashwin P.,Melbourne I.&Nicol M.2001Hypermeander of spirals;local bifurcations and statistical properties.Physica D14,275–300.Baladi V.1999Decay of correlations.In Smooth Ergodic Theory and its Applications, Amer.Maths.Soc.297–325.Biktashev V.N.&Holden.A.V.2001Deterministic Brownian motion in the hyperme-ander of spiral waves.Physica D116,342–382.Brahona M.&Poon C.-S.1996Detection of nonlinear dynamics in short,noisy time series.Nature381,215–217.Cazelles B.&Ferriere R.H.1992How predictable is chaos?Nature355,25–26.Article submitted to Royal Society10G.Gottwald and I.MelbourneCoullet P.&Emilsson K.1996Chaotically induced defect diffusion.In:Instabilities and Nonequilibrium Structures,V(ed.E.Tirapegui and W.Zeller),pp.55–62.Dordrecht: Kluwer.Eckmann J.-P.,Kamphurst S.O.,Ruelle D.&Ciliberto S.1986Liapunov exponents from time series.Phys.Rev.A34,4971–4979.Field M.,Melbourne I.&T¨o r¨o k A.2002Decay of correlations,central limit theorems and approximation by Brownian motion for compact Lie group extensions.Ergod.Th.& Dynam.Sys.To appear.Guckenheimer J.&Holmes P.1990Nonlinear Oscillations,Dynamical Systems,and Bi-furcations of Vector Fields.Appl.Math.Sci.42,New York:Springer.Kawahara T.&Toh S.1988Pulse interactions in an unstable disspative-dispersive non-linear system.Phys.Fluids31,2103–2111.Melbourne I.&Nicol M.2002Statistical properties of endomorphisms and compact group extensions.Preprint,University of Surrey.Melbourne I.&T¨o r¨o k A.2002Central limit theorems and invariance principles for time-one maps of hyperbolicflmun.Math.Physics229,57–71.Nicol M.,Melbourne I.&Ashwin P.2001Euclidean extensions of dynamical systems.Nonlinearity14,275–300.Parker T.S.&Chua L.O.1989Practical Numerical Algorithms for Chaotic Systems.New York:Springer.Parlitz U.&Lauterborn W.1987Period-doubling cascades and devil’s staircases of the driven van der Pol oscillator.Phys.Rev.A36,1428–1434.Press W.H.,Teukolsky S.A.,Vetterling W.T.&Flannery B.P.1992Numerical Recipes in C.Cambridge University Press.Sano M.&Sawada Y.1985Measurement of the Lyapunov spectrum from a chaotic time series.Phys.Rev.Lett.55,1082–1085.Takens F.1981Detecting strange attractors in turbulence.Lecture Notes in Mathematics 898,366–381,Berlin:Springer.van der Pol B.1927Forced oscillations in a circuit with nonlinear resistance(receptance with reactive triode).Philos Mag.43,700.Wolf A.,Swift J.B.,Swinney H.L.&Vastano J.A.1985Determining Lyapunov exponents from a time series.Physica D16,285–317.Article submitted to Royal Society。
fast算子的快速配准
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scheduler
Fig. 1.
Logical structure of an input-queued cell switch
I. I NTRODUCTION Study the past if you would divine the future. - Confucius (c. 551-c. 479 BC) Over the past few years the input-buffered switch architecture has become dominant in high speed switching. This is mainly due to the fact that the memory bandwidth of its packet buffers is very low compared to that of an output-queued or a shared-memory architecture. Furthermore, for an n × n switch, an output-buffered architecture requires a switch fabric with a processing speed of n times the line-rate, whereas an inputbuffered switch requires a fabric with a processing speed as much as the line-rate. Fig. 1 shows the logical structure for an input-queued (IQ) switch. Suppose that time is slotted so that at most one packet can arrive at each input in one time slot. Packets arriving at input i and destined for output j are buffered in a “virtual output queue” (VOQ), denoted here by V OQij . The use of virtual output queues avoids performance degradation due to the head-of-line blocking phenomenon [2]. Let the average cell arrival rate at input i for output j be λij . The incoming traffic n is called admissible if n i=1 λij < 1, and j =1 λij < 1. We assume that packets are switched from inputs to outputs by
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suggested recently by Le Doussal and Giamarchi [14]. They have shown that at the replicasymmetric RG fixed-line for T < Tc RSB is a relevant perturbation. This suggest the possibility of a spontaneous replica-symmetry breaking. Numerical simulations of the weak coupling RSGM show no sign of a transition whatsoever in the equilibrium properties [12]. Our simulations of the random-substrate model, which is better described by the strong-coupling RSGM, exhibit a continuous transition at the analytically predicted temperature [13]. The behavior of the correlations is consistent with the predictions of the GV theory with one-step RSB. For the dynamical behavior the only analytic predictions are from heuristic arguments [15,16] and from RG calculations [6]. The RG predicts anomalous dynamics for T < Tc which is manifested by a temperature-dependent dynamic exponent such that z = 2(1 + 1.78ǫ) and a non-linear response to a small driving force F . The average velocity is predicted to be proportional to F z/2 for small F . The simulations [12] vindicate qualitatively this latter prediction although the coefficient of ǫ is much smaller than that predicted by dynamic RG. In view of these results we study here a non-perturbative self-consistent approach to the dynamics of the system as a complement to the RG calculations. The general approach we take is based on that introduced by Sompolinsky and Zippelius (SZ) [17] for the Sherrington-Kirkpatrick (SK) model. This approach has been extended by Crisanti, Horner, and Sommers for the spherical p-spin interacting spin-glass model [18] and generalized by Kinzelbach and Horner (KH) [19] for fluctuating manifolds in disordered media. We begin by the RSGM Hamiltonian (1) with random-quenched phase with zero mean and correlations: eıγd(x) e−ıγd(x ) = δ (x − x′ ).
The coefficient κ is the stiffness, g is the coupling constant, γ determines the periodicity (we choose γ = 2π ), and d(x) is a random variable such that γd(x) is a random phase uniformly distributed in (0, 2π ]. Random phases for different x are uncorrelated (shortdistance correlations will not affect the conclusions). The variable h(x, t) denotes the vortexline displacement from a periodic lattice or the height of the surface. The controversial issues concern the properties of the low-temperature phase. The quantity of interest is the correlation function: C (x − x′ , t − t′ ) = [h(x, t) − h(x′ , t′ )]
2
,
(2)
where · · · denotes averaging over the thermal noise and the quenched disorder. In the static limit, RG predicts [1–4] that C (x − x′ ) changes behavior from (T /πκ) ln |x − x′ | for T > Tc , to a behavior ∼ ǫ2 (ln |x − x′ |)2 for T < Tc where Tc = κ/π is the critical temperature and ǫ = |1 − T /Tc |. The GV approach yields [7,8] a one-step replica symmetry breaking (RSB) which gives correlations which remain logarithmic but with a different coefficient S (T ) ln |x − x′ | for T < Tc . One study [8] finds S (T ) = Tc /πκ while the other [7] finds a coefficient which increases from this value for temperature below Tc . A potential resolution of the contradiction between the RG and the GV results was 3
1
x > ξ ∗ , they behave as ∼ m−1 T ln |x| where m ≈ T /Tc near Tc , in general agreement with the variational replica-symmetry breaking approach and with recent simulations of the disordered-substrate surface. For strong-coupling the transition becomes first-order. 05.70.Jk, 64.60.Fr, 64.70.Pf, 74.60.Ge
D. Cule and Y. Shapir
Institute for Theoretical Physics University of California Santa Barbara, CA 93106-4030 and Department of Physics and Astronomy University of Rochester Rochester, NY 14627 (February 1, 2008)
NSF-ITP 94-104
Non-Ergodic Dynamics of the 2D Random-Phase Sine-Gordon Model: Applications to Vortex-Glass Arrays and Disordered-Substrate Sat/9410067v1 18 Oct 1994
Typeset using REVTEX 2
Much attention has been given recently to the 2D sine-Gordon model [1–16] with random phases which describes two very interesting disordered systems: (i) An array of flux-lines as obtained when a magnetic field is applied parallel to a type II superconducting film [5], and (ii) A 3D crystalline surface with a disordered substrate [6]. It is also equivalent to a vortex-free XY model with a random field. The intensive theoretical attention is due to diverging results obtained by the renormalization group (RG) [1–4], the Gaussian variational (GV) approximation [7,8], n → 0 Bethe ansatz [9,10], and by two extensive numerical studies [12,13]. The Hamiltonian of the random sine-Gordon model (RSGN) is: H= dx κ [∇h(x, t)]2 − g cos (γ [h(x, t) − d(x)]) . 2 (1)