limit

利用分步傅里叶法求解光纤中脉冲传输的非线性薛定谔方程。(Solves the NLS equation with the split-step method.)

最短路径算法 matlab语言，直接可用(shortest path)

杂草识别，使用gui,代码图像都有，对于做识别杂草很有启发。(Weeds recognition, using GUI, code images have, for identifying weed is very enlightening.)

system identification with OLS input selection

BFGS变尺度法，MATLAB实现，求解函数极值(BFGS variable metric algorithm implemented by MATLAB)

辨识信号白噪声的产生，新手必看。希望对大家帮助(Identification signal generating white noise, Xinshoubikan)

运用遗传算法（GA）实现Schaffer s F6 函数的优化(The use of genetic algorithms (GA) to optimize Schaffer s F6 function)

有一个讲解kpca的个PPT，和一个用MATLAB实现的kpca的程序(There is a PPT explaining KPCA and a KPCA program implemented by MATLAB.)

说明：  利用差分法，将数据拟合，从而可以更好的辨识非线性系统的损失位置。(By using the difference method and fitting the data, the loss location of the non-linear system can be identified better.)

在这项工作中所使用的方法是基于一个由大卫阿诺德教程。 http://online.redwoods.cc.ca.us/instruct/darnold/diffeq/logistic/logistic.pdf 这将运行该Logistic.m带来了图形用户界面。 1。放弃在列的格式文本文件中的x值 2。放弃在山口格式文本文件中的y值 3。的阴谋初始 按钮将绘制的分布 4。 查找适合 按钮，会找到最适合 5。 重置 将删除的情节（虽然我想打扫所有的领域 -没有时间） 5。地下K，糖尿病在下列公式中的值 Ÿ 等于k/（1+进出口（- G *的（十型录））） 6。上证所给出了squred误差之间的拟合函数与实际数据的总和 7。消委会提供的相关合作关系的实际数据拟合功能和效率(The method used in this work is based on a tutorial by David Arnold. http://online.redwoods.cc.ca.us/instruct/darnold/diffeq/logistic/logistic.pdf RUN The Logistic.m this will bring up the GUI. 1. Give the x values on a text file in column format 2. Give the y values on a text file in col format 3. Plot Initial Button will plot the distribution 4. Find Fit button will find the best fit 5. Reset will remove the plot (Although I wanted to clean all the fields- did not have time) 5. K, G, Dm are the values in the following equation y = K./(1+exp(-G*(x-Dm))) 6. SSE gives the sum of squred error between the fitted function and the actual data 7. CC give the correlation co-efficient between the fitted function and actual data)