ZFMMSEFEQ

Tsai Calibration method

偏微分方程的Matlab解法，方便你使用MATLAB来解决偏微分问题！(Solution of Partial Differential Equations Matlab for you to use MATLAB to solve the partial differential problem!)

使用matlab实现PID控制的源码，使用simulink(The realization of the use of PID control matlab source, using simulink)

上传的都是一些没有完成的小程序，希望大家帮帮忙，小女子这厢有礼了(I m a new student)

说明：  实现了sift算法,全部是用matlab写的，很实用(realize the sift)

call matlab function from C++.zip

说明：  Grey Wolf Optimizer MATLAB code for doing optimize your project

说明：  optimal pid control design for a plant using GA. Here ISE is taken as objective function

高速铁路路基沉降是一个复杂的系统过程,涉及不同时期、不同工况,选用单一的预测模型不足以反映路基整体的变形规律.Logistic模型及Verhulst模型的曲线均为S形,可以反映全过程的地基沉降与时间的关系.针对路基填筑期和降水期两个时段利用MATLAB软件编制程序计算模型参数,得到Logistic模型和灰色Verhulst模型,进而求得两者的最优加权组合模型.对比结果表明:在路基填筑期由两者的最优加权组合模型预测得到的结果与实测结果较吻合,拟合精度高于单一模型 而在降水期Verhulst模型的预测值更接近实测值,平均相对误差约为1.75％,拟合精度较高.(High-speed railway subgrade settlement process is a complex system involving different times and in different conditions, the choice of a single forecasting model does not adequately reflect the overall deformation of roadbed. Curve Logistic model and Verhulst models are S-shaped, the whole process can be reflected foundation settlement versus time. against roadbed construction period of two hours and precipitation using MATLAB software programming model parameters, the Logistic model and gray Verhulst model, and then get the optimal model of a weighted combination of the two. comparative results indicate : results and experimental results in the roadbed construction period by the weighted combination of the best of both models predicted than the anastomosis fitting accuracy than a single model precipitation predicted value of Verhulst model closer to the actual value of the average relative error approximately 1.75 fitting accuracy.)

These functions have been developed to watch and analyze EEG/Video/Accelero(3A)/Magneto(3M) data in the context of the Epimouv/Capametrim project. The project aim is to recognized epileptic movement disorders by determining regions of interests on the video and classify motor manifestations thanks to 3A3M sensors.