用matlab仿真，将世界坐标系转化为像素坐标系

调用腾讯的股票查询接口，实现股票在线查询，为了增加可读性，还使用flask搭建了web工程，实现前端展示

模糊C均值聚类图像分割算法的matlab实现，，模糊C均值聚 算法 matlab实现 。图像研究的

FFT在高动态扩频信号捕获中的应用 可用于载波跟踪伪码捕获是一种很好的方法

Simulik仿真环境下的整流器PI双闭环控制

读取txt文件中的数字并在坐标图上绘制曲线，可以实现曲线不停绘制的效果

【实例简介】sliding mode control 英文版的，是学习滑模变结构控制理论的人士必读的书籍，通俗易懂

计算飞机气动特性的软件的说明书，主要应用于概念设计和初步设计阶段TABLE 2AERODYNAMIC OUTPUT AS A FUNCTION OF /CONFIGURATION AND SPEED REGIME● OUTPUT A∨A| LABLED OUTPUT ONLY FOR CON FIGURATIONS WITH STRAIGHT TAPERED SURFACESA OUTPUT ONLY WITH EXPERIMENTAL DATA INPUCONFIGURATION SPEEDSTATIC AERODYN AMIC CHARACTERISTIC OUTPUTDY NAMIC STABILITY CUTPUREGIMECLSUBSONICBODYTRANSONICSUPERSONICHY PERSONICWINGTRANS0NC口▲|口「口百口SUPERSONIC●{a!口口日口HYPERSONIC口!口D口口HORIZONTALTRANS0NCa|▲▲口口|口白SUPERSONIC·a|口口口·『●|●IY PERSONICVERTICAL TAILSUBSONICOR VENTRALTRANSONIC口FINA sUPERSONIC.「百|口HY PERSONICSUBSONICWING-BODYRANSONC口口口o口SUPERSONIC●d|口HY PERSON百口HORIZONTALTRANS0NIc口口口TAIL- BOOYSUPERSONIC口口0口HY PERSONICSUBSONICVERTICAL TAILTRANSONIC口VENTRAL FIN-2 SUPERSONIC.BODY●●|●|●HYPERSONIC口w|NG-B0D￥△ SUBSONIC■口|口|口|口|a口|aaa0|口aaaaTRANSONICO|口TAILSUPERS0NC口百|百口「口。|口HYPERS0NC口口|口口口口口口SUBSON1C●VERTICAL TAILTRANS0NC▲口VENTRAL FINA SUPERSONIC●●|●●HYPERSONICING BOOYSUBSONICHORIZONTAL口日|a口口a「aa}aTRANSONIC|▲|▲▲▲口A SUPERSONIC O。|。口}口|口口|口VERTICAL TAILVENTRAL FINHYPERS0NC口口c1 THE EFFECTS OF JET POW2PELLER POWER, AND GROUND PROXIMITY MAY BE OBTAINED FOR THESE CONFIGURATIONS IF THE REQUIREDNAMELISTS ARE INPUT, THE EFFECTS OF POWER AND GROUND EFFECTS ARE INCLUDED ONLY IN THE SU!C LONGI TUDIN AL STABILITY RESULTS2 DY NAMIC STABILITY RESULTS ARE THE SAME AS WING-BODY3 TKIN VERTICAL TAIL RESULTS MAY BE OBTAINED FOR THESE CONFIGURATIONS IF THE REQUIRED NAMELIST IS INPUTTHESE EFFECTS ARE INCLUDED ONLY IN THE SUBSONIC LATERAL STABILITY DATAREFER TO DATCOM HANDBOOK FOR METHOD LIMITATIONS IF OUTPUT IS NOT OBTAINED5 AVAILABLE ONLY IN COMBINAT:ON WITH A WING OR TAILCC

【实例简介】bp神经网络 simulink 计算机控制与仿真 预测 matlab bp

针对RPCA（鲁棒pca）的pptPCAPCA直观理解PCA矩阵相乘与投影向量B的模为1,则A与B的内积值等于A向B所在直线投影的矢量长度PCA矩阵相乘与坐标空间映射C三BQB为单位向量,相互正交CCBBPCA月gPCASVD=2OOOOO为单位向量,相互正交PCA前特征前特征前特征iiii出补R:_}原图P与前特征前特征前特征ROBUST PCA经典PCA局限性smc∥ Gaussian noises sparse, large errorsclassical PCA outputclassical PCA outputROBUST PCA提出 Robust pca给出:D=A0+E0,恢复A0和EoLoW-rankSparse componentcomponentgross errorsmin rank(A)+rIEllo subj A+E=D低秩:rank(A)=#{0(A)≠0}sparse, large errors稀疏:‖E|0=#{E;≠0}Not always -original problem is NP-hard

方便快捷免安装哦，DEA的基础模型即CCR模型在用来研究具有多个输入输出变量的的技术效率时十分有效，但其假设条件是所有评价单元都是规模有效的，其模型运算结果实际上是一种纯技术效率。