PSCAD

1000kV高压输电线的三维电磁场计算的matlab程序(Three-dimensional electromagnetic)

调试所用Matlab版本是6.5，gatbx-toolbox是调试过程中使用的工具箱，有些热心的研友已经提供了该工具箱，之所以再贴一遍，是因为6.5版本下调试该工具箱会出现一些错误提示，在该版本中已经进行了改正，使用该工具箱只需要将压缩包下的gatbx和gatbxTest_fns加入到Matlab搜索路径即可，另外gatbx-origin是Sheffield大学提供的原始版本的工具箱，未做任何改动，两者可做比较之用 3.gatbx-example是算例调试源码和各算例的调试说明，大家可以对照原书进行比较 4.窃以为gatbx工具箱较之Matlab7新版下的gads工具箱更能让人熟悉和了解遗传算法的原理和计算过程，虽然在可视化方面不及gads 5.学习遗传算法如果首先用熟相关工具箱的话，能够先培养信心，然后更有兴趣去专研和了解具体的算法，如果就用现有的工具箱就能解决问题的话，不了解其详细的算法也未尝不可，毕竟不是每个研友都能向see_moonlight那样开发自己的工具箱吧, ( The debugging uses the Matlab edition is 6.5, gatbx-toolbox debugs the toolbox which in the process uses, somewhat warm-hearted ground the friend already to provide this toolbox, the reason that pasted again, was because under 6.5 editions debugged this toolbox to be able to present some wrong prompts, had already carried on the correction in this edition, will use this toolbox only to need to compress package of under gatbx and gatbx Test_fns joins to the Matlab search path then, moreover gatbx-origin was the primitive edition toolbox which the Sheffield university provided, has not made any modification, both might make the comparison are the example debugging sound code and various examples debugging explained with 3.gatbx-example, everybody might compare the original bookCarries on comparison 4. in my opinion under the gatbx toolbox s to be able to let the human compared with Matlab7 update gads toolbox be familiar with the understanding genetic algorithm principle and)

有关交互多模型matlab实现算法，仿真场景可以自己设计(Related to interactive multi-model algorithm matlab, simulation scenarios can be designed)

LMS filter to solve the system identification problem

基于matlabGUI界面下的电子双缝衍射实验的现象模拟，设置的可输入参数有：缝宽a，双缝间距b，加速电压U，缝屏距离D和电子数目n(Electronic Computer Simulation of double-slit diffraction)

这是一本关于Windows下内存映射文件相关内容的书籍， 通过它可以操作文件，它的速度是I/O操作的20倍，及其适合大文件的操作(This is a book about Windows memory mapping document of relevant books, through which it can file, its speed is I/O operation20 times, and is suitable for large files )

GUI字符识别，用于数字的自动识别，识别效率高(this is GUI example)

TSP solved by genetic algorithms

给定一个样本集exset，样本数为M，每个样本点是N维向量，对于给定目标点d，d也为N维向量，要从exset中找出与d距离最近的k个点(k<=N)(knn code c++)

machine learning 方面有关于The Bayes Classifier的资料，语言是python(machine learning aspects have The Bayes Classifier information about language is python)