Raporlar

potential field method

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)

关于严格耦合波发分析光栅等的相关matlab仿真(About rigorous coupled-wave analysis of hair and other related grating matlab simulation)

matlab中wave文件的频率解析代码，欢迎使用(Matlab which documents the frequency wave analysis code, welcomed the use of)

说明：  粮食产量预测的matlab实现，基于给定的几年的粮食产量，预测出后一年的产量，属于原创，有论文，有代码(Grain output forecast matlab implementation, based on given years of food production, predicted after the year of production, are original, papers, a code)

支持向量机算法，可很好的用于分类问题，广发用于人脸识别，步态识别中！(Support vector machine algorithm, can be a good issue for classification, GF for face recognition, gait recognition!)

Using BBO AI image denoising is done for better result in matlab analysis done on the baseses of parameters.......................

用于计算二维相关光谱，P为同步二维相关光谱，Q为异步二维相关光谱； % data为m*n的光谱m为波长点数，n为样品数；(Two dimensional correlation spectroscopy)

matlab 模式识别识别数据，循环处理(Matlab pattern recognition to identify data processing cycle)

Nothing to do, we need to learn how to trick something on the webs