支持向量回归

1000款MT4指标源码文件，包含各种金典指标(蝴蝶指标)。

详细介绍了 如何用psim软件实现永磁电动机的矢量仿真Vol 16 No900系统仿真学报May 2004对永磁同步电机(PMSM)数学模型的电压方程式(4进行2.4SPWM模块abc/da坐标变换,可得dq坐标系下的电压方程式(8):正弦脉宽调制,它是以正弦波作为基准的调制波,三角0d=rid+pod-oo(8)波作为载波,当调制波与载波相交时,由他们的交点确定变4=rig+ peyd+oou频器开通的时间,从而产生等幅不等宽的脉冲波形。图8是其中SPWM变频器的控制回路,一组三相对称的正弦参考信号Og =lsig, Od=liia+OatUa、Ub、Ua与三角波参考信号U,相比较,作为三相桥臂6个功率开关元件的控制信号。式中:、口—d、q相定子磁链;a—转子磁场对定子的交链;、Ld、q相绕组申感。图5、图6中的U、U子模块的功能就是实现方程式(8),心子模块的底层结构如图5所示,U子模块的底层结构如图6所示。Kp1载波他号[Iq图8SPWM变频器控制回路图5U4子模块结构框图2.5速度控制模块速度控制模块的结构较为简单,如图9所示,输入:参考转速和实际转速的差值,输出:q相电流参考值lqrfq其中,Kp为P控制器中P(比例)的参数,KpT为P控制器中I(积分)的参数, Saturation饱和跟幅模块将输出的q相参考电流幅值限定在要求范围內。图6U子模块结构框图Ia ret23坐标变换模块坐标变换模块实现的是d旋转坐标系下的两相相电压x口Un、Ug向abc静止坐标系的三相电压Ua、Ub、U的等Kp/T效变换。与矢量控制模坎类似,d2abc模块实现的是dq两相向ab三相的变换,模块的底层结构嬗鹵7腙示,功由图9速度控制模块结构框图dq/abc电流变换方程式(9戾实现26电压逆变模块C。=Ucos6+ sine+Ucos(-120°)+Usin(-120°)+U电压逆变模块实现的是逆变器功能,输入为SPWM模U= U, cos(日+12°)+Usin(日+120°)+U块给出的逆变控制信号,输出为三相相电压。图10是电压逆变模块结构框图。该模块可采用中提供的通用逆变模块搭建,只需3对iT功率开关器件,反向并联续流二极管c根据SPWM模块给出的控制信号,控制6个开关器件顺序四导通和关断,从而产生三相相电压输出。2s0本本去J2PI/3图10电压逆变模块结构框图图7dq2abc模块结构框图C1994-2010chinaAcadcmicJOurnalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nctVoL 16 No 5May 2004纪志成,等∶基于PSIM永磁同步电机矢量控制系统的仿真建模·901·3仿真结果组电感L=0.06H,转动惯量J=000179kgm2,额定转速1500rmin,极对数n=2。为了验证所设计的PMSM根据上述所建立的PMSM矢量控制系统的仿真模型,矢量控制系统仿真模型的静、动态性能,系统带负载T在PSIM6.0的仿真环境下进行了仿真,PMSM电机参数设Nm起动,得到系统转、转矩、d-q两相相电流仿真曲置为:电机功率p=500W,直流电压l=220V,定子相绕线如图114所示。组电阻R=432,定子d相绕组电感L=0027H,q相绕200013001200110015000010008v8006500003001.0000200400100t(s)t(3)图11转速响应曲线图12转矩响应曲线10016.001400-0504000.002005c01.00200300500t(s)图13d相电流波形图14q相电流波形由仿真滅形可以看出:在n=150 Or/in的参考转速下,性能威者模拟相同的实验糸件比较不同控制策喲的优劣,系统带负载启动响应快速且平稳,如两相电流波形较为理汋分析和设计永磁冋步电机控制系统提供了有效地手段和想,稳态运行时转速无静差。仿真结果证明了本文所提岀的工具,也为实际电机控制系统的设计和调试提供了新的思这种新型PMsⅥ仿真建模方法的有效性。路。结论参考文献:[I P Pillay, R Krishnan. Modeling, simulation, and analysis of本文在分析PMSM数学模型的基础上,提出了基于permanent-magnet motor drives, Part 2: The permanent-magnetPSIM的PMSM控制系统仿真模型。该控制系统采用速度环synchronous motor drive [] IEEE Trans. on Industry ApplicationsPⅠ控制和电流环矢量控制的双闭环控制方法并在1989,25(2):265-273[21 Pragasan Pillay, R Krishnan. Modeling of permanent magnet motorPSIM60 SIMCAD环境下对该控制系统进行了设计与仿真。drives[J]. IEEE Trans on Industry Electronics, 1988. 35(4): 537-541仿真结果表明:波形符合理论分析,系统能平稳运行,具有③CiP. Zhu jG.IaQ,P,ctal. Simulation of nonlinear switche较好的静、动态特性。采用该PMSM仿真模型,可以十分eluctance motor drives with PSIM[C]. Proceedings of Electrica便捷地实现、验证控制算法,只需对部分功能模块迸行替换Machines and Systems, 2001, 2: 1061-10644]纪志成薛花沈艳霞.永磁同步电机调速系统的樸糊PⅠ智能控制或修改,就可实现控制策珞的改换或改进,不仅可以节省控新方法[电工技术学报,2003,18(6:53-58制方案的设计周期,快速验证所设计的控制算法,更可以充「5Y. SJeon, H.S. Mok,G, H. Choe,cta. a new simulation model of分利用计算机仿真的优越性,通过修改系统参变量或人为加PMSM motor with rcal back EMF wavcform[C]. Procccding from入不同扰动因素来考察不同实验条件下电机系统的动、静态Computers in Power Electronics, 2000, 16-18: 217-220C1994-2010chinaAcadcmicJOurnalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct

c#编写的常用测量程序,含坐标正反算、角度弧度转换、矩阵计算(加减乘求逆)、导线测量数据处理、水准测量数据处理、三角高程测量数据处理等程序。内涵源代码及代码注解,可供学习和生产使用

二分法查找C++代码，包括递归健忘版、非递归健忘版、递归非健忘版、非递归健非忘版，用到了stl里的vector和模板的知识

SpaceClaim中文版-官方(教程),这方面的资料比较少，版本是依据19.1，分享给大家

对jaffe人脸库进行识别测试，将jaffe人脸库分为训练集和测试集两部分，首先对图片进行LBP+LPQ特征提取，然后svm分类识别，统计识别率

内含STM32F103C8T6电路原理图及PCB图

射线追踪的matlab仿真代码，计算到达功率，画出传播路径图

【实例简介】内含有protuse仿真电路，程序，论文，是花两个月时间做的毕业论文，希望能对有需要的朋友有帮助

这是一本关于随机规划比较全面的书！比较难，不太容易啃，但是读了之后收获很大。这是高清版的！To Julia, Benjamin, Daniel, Nalan, and Yael;to Tsonka Konstatin and Marekand to the memory of feliks, Maria, and dentcho2009/8/20pagContentsList of notationserace1 Stochastic Programming ModelsIntroduction1.2 Invento1.2.1The news vendor problem1.2.2Constraints12.3Multistage modelsMultiproduct assembl1.3.1Two-Stage Model1.3.2Chance Constrained ModeMultistage modelPortfolio selection131.4.1Static model14.2Multistage Portfolio selection14.3Decision rule211.5 Supply Chain Network Design22Exercises2 Two-Stage Problems272.1 Linear Two-Stage Problems2.1.1Basic pi272.1.2The Expected Recourse Cost for Discrete Distributions 302.1.3The Expected Recourse Cost for General Distributions.. 322.1.4Optimality Conditions垂Polyhedral Two-Stage Problems422.2.1General Properties422.2.2Expected recourse CostOptimality conditions2.3 General Two-Stage Problems82.3.1Problem Formulation, Interchangeability482.3.2Convex Two-Stage Problems2.4 Nonanticipativity2009/8/20page villContents2.4.1Scenario formulation2.4.2Dualization of Nonanticipativity Constraints2.4.3Nonanticipativity duality for general Distributions2.4.4Value of perfect infExercises3 Multistage problems3. 1 Problem Formulation633.1.1The general setting3.1The Linear case653.1.3Scenario trees3.1.4Algebraic Formulation of nonanticipativity constraints 7lDuality....763.2.1Convex multistage problems·763.2.2Optimality Conditions3.2.3Dualization of Feasibility Constraints3.2.4Dualization of nonanticipativity ConstraintsExercises4 Optimization models with Probabilistic Constraints874.1 Introduction874.2 Convexity in Probabilistic Optimization4.2Generalized Concavity of Functions and measures4.2.2Convexity of probabilistically constrained sets1064.2.3Connectedness of Probabilistically Constrained Sets... 113Separable probabilistic Constraints.1144.3Continuity and Differentiability Properties ofDistribution functions4.3.2p-Efficient Points.1154.3.3Optimality Conditions and Duality Theory1224 Optimization Problems with Nonseparable Probabilistic Constraints.. 1324.4Differentiability of Probability Functions and OptimalityConditions13344.2Approximations of Nonseparable ProbabilisticConstraints134.5 Semi-infinite Probabilistic Problems144E1505 Statistical Inference155Statistical Properties of Sample Average Approximation Estimators.. 1555.1.1Consistency of SAA estimators1575.1.2Asymptotics of the saa Optimal value1635.1.3Second order asStochastic Programs5.2 Stoch1745.2.1Consistency of solutions of the SAA GeneralizedEquatio1752009/8/20pContents5.2.2Atotics of saa generalized equations estimators 1775.3 Monte Carlo Sampling Methods180Exponential Rates of Convergence and Sample sizeEstimates in the Case of a finite Feasible se1815.3.2Sample size estimates in the General Case1855.3.3Finite Exponential Convergence1915.4 Quasi-Monte Carlo Methods1935.Variance-Reduction Techniques198Latin hmpling1985.5.2Linear Control random variables method200ng and likelihood ratio methods 205.6 Validation analysis5.6.1Estimation of the optimality g2025.6.2Statistical Testing of Optimality Conditions2075.7Constrained Probler5.7.1Monte Carlo Sampling Approach2105.7.2Validation of an Optimal solution5.8 SAA Method Applied to Multistage Stochastic Programmin205.8.1Statistical Properties of Multistage SAA Estimators22l5.8.2Complexity estimates of Multistage Programs2265.9 Stochastic Approximation Method2305.9Classical Approach5.9.2Robust sA approach..23359.3Mirror Descent sa method235.9.4Accuracy Certificates for Mirror Descent Sa Solutions.. 244Exercis6 Risk Averse Optimi2536.1 Introductio6.2 Mean-Risk models.2546.2.1Main ideas of mean -Risk analysis546.2.2Semideviation6.2.3Weighted Mean Deviations from Quantiles.2566.2.4Average value-at-Risk2576.3 Coherent risk measures2616.3.1Differentiability Properties of Risk Measures2656.3.2Examples of risk Measures..2696.3.3Law invariant risk measures and Stochastic orders2796.3.4Relation to Ambiguous Chance Constraints2856.4 Optimization of risk measures.2886.4.1Dualization of Nonanticipativity Constraints2916.4.2Examples...2956.5 Statistical Properties of Risk measures6.5.IAverage value-at-Ris6.52Absolute semideviation risk measure301Von mises statistical functionals3046.6The problem of moments306中2009/8/20page xContents6.7 Multistage Risk Averse Optimization3086.7.1Scenario tree formulation3086.7.2Conditional risk mappings3156.7.3Risk Averse multistage Stochastic Programming318Exercises3287 Background material3337.1 Optimization and Convex Analysis..334Directional Differentiability3347.1.2Elements of Convex Analysis3367.1.3Optimization and duality3397.1.4Optimality Conditions.............3467.1.5Perturbation analysis3517.1.6Epiconvergence3572 Probability3597.2.1Probability spaces and random variables7.2.2Conditional Probability and Conditional Expectation... 36372.3Measurable multifunctions and random functions3657.2.4Expectation Functions.3687.2.5Uniform Laws of Large Numbers...,,3747.2.6Law of Large Numbers for Random Sets andSubdifferentials3797.2.7Delta method7.2.8Exponential Bounds of the Large Deviations Theory3877.2.9Uniform Exponential Bounds7.3 Elements of Functional analysis3997.3Conjugate duality and differentiability.......... 4017.3.2Lattice structure4034058 Bibliographical remarks407Biibliography415Index4312009/8/20pageList of Notationsequal by definition, 333IR", n-dimensional space, 333A, transpose of matrix(vector)A, 3336I, domain of the conjugate of risk mea-C(X) space of continuous functions, 165sure p, 262CK, polar of cone C, 337Cn, the space of nonempty compact sub-C(v,R"), space of continuously differ-sets of r 379entiable mappings,176set of probability density functions,I Fr influence function. 3042L, orthogonal of (linear) space L, 41Sz, set of contact points, 3990(1), generic constant, 188b(k; a, N), cdf of binomial distribution,Op(), term, 382214S, the set of &-optimal solutions of theo, distance generating function, 236true problem, 18g(x), right-hand-side derivative, 297Va(a), Lebesgue measure of set A C RdCl(A), topological closure of set A, 334195conv(C), convex hull of set C, 337W,(U), space of Lipschitz continuousCorr(X, Y), correlation of X and Y 200functions. 166. 353CoV(X, Y, covariance of X and y, 180[a]+=max{a,0},2ga, weighted mean deviation, 256IA(, indicator function of set A, 334Sc(, support function of set C, 337n(n.f. p). space. 399A(x), set ofdist(x, A), distance from point x to set Ae multipliers vectors334348dom f, domain of function f, 333N(μ,∑), nonmal distribution,16Nc, normal cone to set C, 337dom 9, domain of multifunction 9, 365IR, set of extended real numbers. 333o(z), cdf of standard normal distribution,epif, epigraph of function f, 333IIx, metric projection onto set X, 231epiconvergence, 377convergence in distribution, 163SN, the set of optimal solutions of the0(x,h)d order tangent set 348SAA problem. 156AVOR. Average value-at-Risk. 258Sa, the set of 8-optimal solutions of thef, set of probability measures, 306SAA problem. 181ID(A, B), deviation of set A from set Bn,N, optimal value of the Saa problem,334156IDIZ], dispersion measure of random vari-N(x), sample average function, 155able 7. 2541A(, characteristic function of set A, 334吧, expectation,361int(C), interior of set C, 336TH(A, B), Hausdorff distance between setsLa」, integer part of a∈R,219A and B. 334Isc f, lower semicontinuous hull of funcN, set of positive integers, 359tion f, 3332009/8/20pageList of notationsRc, radial cone to set C, 337C, tangent cone to set C, 337V-f(r), Hessian matrix of second orderpartial derivatives, 179a. subdifferential. 338a, Clarke generalized gradient, 336as, epsilon subdifferential, 380pos w, positive hull of matrix W, 29Pr(A), probability of event A, 360ri relative interior. 337upper semideviation, 255Le, lower semideviation, 255@R. Value-at-Risk. 25Var[X], variance of X, 149, optimal value of the true problem, 1565=(51,……,5), history of the process,{a,b},186r, conjugate of function/, 338f(x, d), generalized directional deriva-g(x, h), directional derivative, 334O,(, term, 382p-efficient point, 116lid, independently identically distributed,1562009/8/20page xlllPrefaceThe main topic of this book is optimization problems involving uncertain parametersfor which stochastic models are available. Although many ways have been proposed tomodel uncertain quantities stochastic models have proved their flexibility and usefulnessin diverse areas of science. This is mainly due to solid mathematical foundations andtheoretical richness of the theory of probabilitystochastic processes, and to soundstatistical techniques of using real dataOptimization problems involving stochastic models occur in almost all areas of scienceand engineering, from telecommunication and medicine to finance This stimulates interestin rigorous ways of formulating, analyzing, and solving such problems. Due to the presenceof random parameters in the model, the theory combines concepts of the optimization theory,the theory of probability and statistics, and functional analysis. Moreover, in recent years thetheory and methods of stochastic programming have undergone major advances. all thesefactors motivated us to present in an accessible and rigorous form contemporary models andideas of stochastic programming. We hope that the book will encourage other researchersto apply stochastic programming models and to undertake further studies of this fascinatinand rapidly developing areaWe do not try to provide a comprehensive presentation of all aspects of stochasticprogramming, but we rather concentrate on theoretical foundations and recent advances inselected areas. The book is organized into seven chapters The first chapter addresses modeling issues. The basic concepts, such as recourse actions, chance(probabilistic)constraintsand the nonanticipativity principle, are introduced in the context of specific models. Thediscussion is aimed at providing motivation for the theoretical developments in the book,rather than practical recommendationsChapters 2 and 3 present detailed development of the theory of two-stage and multistage stochastic programming problems. We analyze properties of the models and developoptimality conditions and duality theory in a rather general setting. Our analysis coversgeneral distributions of uncertain parameters and provides special results for discrete distributions, which are relevant for numerical methods. Due to specific properties of two- andmultistage stochastic programming problems, we were able to derive many of these resultswithout resorting to methods of functional analvsisThe basic assumption in the modeling and technical developments is that the proba-bility distribution of the random data is not influenced by our actions(decisions). In someapplications, this assumption could be unjustified. However, dependence of probability dis-tribution on decisions typically destroys the convex structure of the optimization problemsconsidered, and our analysis exploits convexity in a significant way