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说明：  经典图书Courant, Hilbert - Methods of Mathematical Physics 第二卷，50年不倒的经典图书，值得一看，是学习数学物理方法的经典名著(Classic books Courant, Hilbert- Methods of Mathematical Physics II, 50 years will not be brought down classic books, worth a visit is to study methods of mathematical physics classics)

gm(1,1)matlab 代码，适合各种预测，并对结果给出误差(gm(1,1))

该文件是间接式双级矩阵变换器的开环仿真模型。输出结果符合理论推导。采用s函数编程的方法实现空间矢量调制策略(This file is indirect two-stage matrix converter of the open loop simulation model. Output the results accord with theoretical derivation. The s function programming method of space vector modulation strategy. )

直方图均衡源代码，从直方图统计到256级灰度转变并显示(Source histogram equalization)

说明：  truss deflection analysis

说明：  用于计算最小均方误差的代码，包含了归一化的uniform函数(Used to calculate the minimum mean square error of the code, including the normalization of the uniform function)

matlab中极坐标函数的改进，坐标轴刻度可以为负数，输入参数见注释，有问题email联系，可在matlab命令行中直接运行，如： my_polar2([0,20,30]*pi/180,[-10,0,10], -r ,-20,30)(Matlab polar coordinates function improved, axes calibration can be negative, the input parameters, see Notes email problems can be contacted in Matlab command line direct operations, such as : my_polar2 ([0,20,30]* pi/180 [-10,0,10],- r,-20,30))

说明：  维纳滤波器的原理及实现,有详细的推导过程(Principle and realization of the Wiener filter, a detailed derivation)

BNB20 Finds the constrained minimum of a function of several possibly integer variables. ％ Usage: [errmsg,Z,X,t,c,fail] = ％ BNB20(fun,x0,xstatus,xlb,xub,A,B,Aeq,Beq,nonlcon,settings,options,P1,P2,...) ％ ％ BNB solves problems of the form: ％ Minimize F(x) subject to: xlb <= x0 <=xub ％ A*x <= B Aeq*x=Beq ％ C(x)<=0 Ceq(x)=0 ％ x(i) is continuous for xstatus(i)=0 ％ x(i) integer for xstatus(i)= 1 ％ x(i) fixed for xstatus(i)=2 ％(BNB20 Finds the constrained minimum of a fu nction of several possibly integer variables. % Usage : [errmsg, Z, X, t, c, fail] =% BNB20 (fun, x0, xstatus, xlb, xub, A, B, Aeq, Beq, nonlcon. settings, options, P1, P2, ...)%% BNB solves problems of the form : Minimize% F (x) subject to : xlb)

这是基于傅里叶变化的压缩感知稀疏分解方法，使用的是匹配追踪算法，对初学者很有帮助(This is based on Fourier transform of compressed sensing sparse decomposition method, using the matching pursuit algorithm, useful for beginners)