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HL计算工程结构可靠度的简单算例2 包含1.例题文件，2.HL法计算，3.Matab自带抽样，4.自编monte carlo抽样(HL Method and Monte Carlo Method for Solving Reliability Index of Structures)

看了一段时间的3GPP的信道建模，所以自己动手写了一个（参考了该协会的标准代码，不过改良了一些，执行效率更高）(the 3GPP SCM model building. Comparing to the original code, this task is more efficient and clearer.)

计算正则化模糊神经网络的输出值，分为五层，包括正则化，模糊化和输出层，内有注释。(Output value calculated Regularization Fuzzy Neural Network, is divided into five layers, including regularization, blurring and output layer, within a comment)

该文件是关于支持向量回归机的代码，其中包括数据预处理的信息。(The file is about support vector regression machine code, including data pretreatment of information.)

二分搜索法 其中包含三个文件求其解析解太难 用二分法 快速搜索 并保证其一定的精度 (Binary search algorithm to find the optimal solution by dichotomy)

这是一个matlab编写的CRC冗余校验编解码程序，里面采用了美国通用的16位CRC交验多项式，(This is a matlab prepared codec CRC redundancy check procedure, which uses the U.S. General inspection of the 16-bit CRC polynomial,)

decisiontree implementation in matlab

说明：  分别用中心极限定理和box-muller法生成高斯噪声(Central limit theorem were used to box-muller method and the Gaussian noise generated)

基于MATLAB的LDPC码编译设计，是ＬＤＰＣ的基础程序，适合初学者(MATLAB-based LDPC code compiler design is based LDPC program, suitable for beginners)

坐标转换（四参数） [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end (Coordinate conversion： [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end )