Matlab-mathematical-modeling

在Matlab/Simulink构造一缓慢时变线性系统。试根据系统的输入生产数据分别用带遗忘因子最小二乘法和广义最小二乘法辨识系统的参数。(in Matlab/Simulink constructed a slow time-varying linear systems. Examination under the input production data were used to bring the forgotten factor method of least squares and generalized least squares method recognition system parameters.)

说明：  LMS算法的MATlab仿真分析，结果表明LMS算法有较好的噪声消除效果！(LMS algorithm MATlab simulation results show that the LMS algorithm has better noise reduction effect!)

本文基于毕肖普法和蒙特卡洛法,利用MATLAB编程软件计算了边坡结构的失效概率Pf和对应可靠指标β.在计算上述Pf和对应β时,考虑了土体三个主要基本参数(重度γ、内摩擦角(Ψ)、粘聚力c)的随机特性.利用MATLAB程序的计算结果表明,随机变量对边坡可靠度的影响与基本随机参数对抗滑反力的贡献大小有关,即,贡献大的基本随机参数,它的随机波动对结构失效概率Pf的影响也较大,反之亦然.另外,基于MATLAB的算例计算结果还表明:边坡结构失效概率Pf对内摩擦角(Ψ)较敏感,而对重度y和粘聚力c次之.其次,当同时考虑三个随机变量的联合波动对Pf的影响时发现,当它们在小范围内随机波动时,其联合特性对Pf的影响较敏感,但当它们在大范围内随机波动时,Pf主要受敏感随机变量的影响,而三个随机变量联合作用对Pf的影响和敏感随机变量(Ψ)单独作用时相差不大.由此可见,在实际边坡工程可靠性分析中,土体内摩擦角起主要作用,不容忽视.(Based Bishop method and Monte Carlo method, using MATLAB programming software to calculate the failure probability Pf and the corresponding slope reliability index β structure in the calculation of the above Pf and the corresponding β, consider the three main basic soil parameters (severe γ internal friction angle (Ψ), cohesion c) of the random nature of calculation results using MATLAB program shows that the influence of random variables on the reliability of the basic random slope parameters against anti- slip contribution to the size of the force , that is , the contribution the basic parameters of large random , random fluctuations which affect the probability of structural failure Pf is also larger , and vice versa addition, the calculation based on MATLAB numerical results also show that : the structure of slope failure probability Pf internal friction angle (Ψ) more sensitive , while severe and cohesion c y followed . Secondly, when taking into account the impact of the joint fl)

Motion Tracking =============== This tarball contains all code required to run the tracking algorithm on a sequence of images. Run the file run_tracker.m in Matlab and follow the instructions. You will need to have a directory of sequentially numbered images available. After entering the path and file types the tracker will begin processing. Once the data window appears the algorithm begins building a background model and attempts to track objects. By clicking on any of the four subwindows you can investigate the background representation (a Mixture of Gaussians) of any pixel. The two windows that then appear display the mixture once as a two-dimensional scatter plot (ignoring the blue colour component), and once as a one-dimensional evolution of the red colour component only. These plots make the internal processing visible and should help determining suitable parameters to be set in mixture_parameters.m.

Removing noise from an image in matlab

说明：  本程序是利用小波包进行图像的压缩的简单的编程(This procedure is the use of wavelet packet image compression for simple programming)

时间序列工具箱,包含大量的时间序列处理函数，非常好用的工具箱！(Time series toolbox contains a large number of time series processing function, very useful toolbox!)

Face recognitiion and detection system in matlab

说明：  实现电磁波在两端为PEC板时的一维FDTD程序(Realize the one dimension FDTD program when the electromagnetic wave is PEC board at both ends)

说明：  该程序是用matlab语言实现的关于瑞利信道多径传输的多谱勒效应的描述(that the procedure was used Matlab language on the Rayleigh Channel multipath transmission of the description of Doppler effect)