CUMCM-MATLAB

蒙特卡洛模拟法是当系统中各个单元的可靠性特征量已知，但系统的可靠性过于复杂，难以建立可靠性预计的精确数学模型或模型太复杂而不便应用则可用随机模拟法近似计算出系统可靠性的预计值。随着模拟次数的增多，其预计精度也逐渐增高。由于需要大量反复的计算，一般均用计算机来完成。本文件用来求解医院排队论问题(Monte Carlo simulation when the system reliability characteristics of each unit volume is known, but the reliability of the system is too complicated, difficult to establish accurate mathematical model of reliability prediction models are too complex or inconvenient applications are available stochastic simulation method to approximate calculation the expected value of system reliability. With the increase in simulation times, which is expected to gradually increase precision. As the need for a large number of repeated calculations, generally using the computer to complete. This document is used to solve the problem of queuing theory Hospital)

matlab处理图像代码，很不错，值得看看(matlab image processing code, very good, worth a look)

This is my power electronic lecture note on diode rectification.

图像线性滤波、热传导方程滤波、Gabor图像锐化和动态边界算法(Image filtering, heat conduction equation filter, Gabor image sharpening algorithms and dynamic boundary)

说明：  国外的时间序列分形的库函数，对于时间序列研究有一定帮助(Fractal time series of foreign library functions, for the time series study will definitely help)

[matlab source code]CPUmeultithread

用c-c方法求相空间重构，计算关联积分时间延迟的程序设计(With the cc method, to calculate phase-space reconstruction to calculate the time delay associated with integration of program design)

针对阵列天线的模拟波束形成，在固定角度上阵列输出最大值。(For beamforming of an array antenna, the array outputs a maximum at a fixed angle.)

应用克里金插值法解决测井数据匹配的问题，通过旧井数据得出邻近的新井的数据(Application of Kriging method to solve the problem log data matching, data obtained by the old well near the new well data)

介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。(Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.)