EMCP21511

三次样条插值（简称Spline插值）是通过一系列形值点的一条光滑曲线，数学上通过求解三弯矩方程组得出曲线函数组的过程。本函数中采用第一类边界值(Cubic spline interpolation (referred Spline interpolation) through a series of key points of a smooth curve, mathematically by solving the three-moment equations derived curve function group process. This function uses the first boundary value)

MATLAB，多目标优化，使用SQP算法(MATLAB, multi-objective optimization using SQP Algorithm)

说明：  快速三次平滑滤波算法为工程人员在对数据进行滤波时提供极大的方便(three rapid smoothing filter algorithm works in the data filtering provides great convenience)

进行主成分分析的多种方法，简单和复杂的，适合初学者使用(For a variety of methods of principal component analysis, simple and complex, suitable for beginners to use)

此为基于图像配准的傅立叶变换源代码，已精心调试过，请放心使用.(This is the image registration based on the Fourier Transform source code, has been painstakingly debugging, please rest assured that use.)

实现高斯厄米特积分，可以直接更换f函数，得到所求的积分(High the Sie Mitt integral to replace the f function, ask the integral )

一本介绍半解析数值方法的经典之作，对从事数值分析及方法研究和应用的科研人员是不错的参考资料(Introduce a semi-analytical numerical method of classic, engaged in numerical analysis and methods of research and application of scientific research personnel is a good reference)

计算低速气动力的涡格法程序，来自NASA，可信度高(This is a vortex lattice aerodynamic program from the low speed aerodynamics group at NASA Langley)

converts binary sequence to decimal sequence

Matlab codes for Robust PCA multivariate control chart(Robust PCA multivariate control chart mainly consists two steps: Step1 Calculates the robust mean and the robust covariance of original dataset using the minimum covariance determinant (MCD). In MCD technique, finding a subset containing half of the data such that its covariance matrix has the lowest determinant, then using this subset to calculate the robust mean and the robust covariance matrix (Hubert, Rousseeuw, & Branden, 2005) Step2 Standardize data using robust mean and robust standard deviation from Step1. Apply PCA analysis, calculate the principalcomponent score matrix Y=ZA, where Z is the robust standardized data matrix, and A is p*p matrix of eigenvectors (also called principalcomponents))