144434smartcard

地球物理反问题的反演的正则化方法及弹性波正则化的应用(inversion algorithms )

支持向量机点点滴滴matlab版，内容不错较为新颖 有很强的实用价值(More novel support vector machine bit by bit matlab version has a strong practical value)

遗传算法解决作业车间调度问题，,这一M文件计算目标函数值。(Genetic algorithms to solve the job shop scheduling problem,, the M-file to calculate the value of the objective function....)

排队买饭程序：利用队列实现，可以有好朋友一起排队。(Queuing to buy food program: the use of queues to achieve, you can have friends lined up together.)

说明：  本套资料,包含了各种Matlab和C++混合编程的例子和理论.是研究Matlab 和VC CBuilder的最权威和经典的例子.在网上搜集的时候发了一个下午的时间。结果发现内容太少了.不得不到我们学校的图书馆去找了。找遍了整个中国期刊全文数据库(CJFD),才找到了这几篇对与编程人员来说的有帮助和参考的文章了。发了我好几块钱了，希望对那些Matlab与Vc和Cbuilder混合编程的人员有很大的帮助了.(the sets of data, including a variety of mixed Matlab and C programming examples and theory. Research VC CBuilder Matlab and the most authoritative and classic examples. The online collection of hair at a time. The results showed very little content. Our school had to find a library. Grateful the entire CJFD (CJFD), they will find this program with a few pairs of officers help and reference of the article. I made a few good money in the hope that those with vitamin C and Matlab Cbuilder mixed programming staff have great help.)

GSA,万有引力算法用于求解智能优化问题,效果特好!!(GSA, Gravitation intelligent optimization algorithm is used to solve problems, good special effects!!)

HsT2Hlp_Demo,中信证券api简单的api示例(HsT2Hlp_Demo, CITIC Securities api api simple example)

kalman递归理论的详细讲解,通谷易懂,想弄明白kalman理论的同志一看便知!(a easy understanding for Kalman theory.it is very useful for new leaner! )

PUMA560仿真系统,基于MATLAB的robottics toolbox工具箱. (PUMA560 simulation system based on MATLAB robottics toolbox, toolbox.)

1,、设置变量和参量： 　　X(n)为输入向量，或称为训练样本 　　W(n)为权值向量 　　e(n)为偏差 　　d(n)为期望输出 　　y(n)为实际输出 　　η为学习速率 　　n为迭代次数 　　2、初始化，赋给w(0)各一个较小的随机非零值，令n=0 　　3、对于一组输入样本x(n)和对应的期望输出d，计算 　　e(n)=d(n)-X^T(n)W(n) 　　W(n+1)=W(n)+ηX(n)e(n) 　　4、判断是否满足条件，若满足算法结束，若否n增加1，转入第3步继续执行(, set the variables and parameters: X (n) is the input vector, otherwise known as the training sample W (n) for the weight vector e (n) for the deviation d (n) is the desired output y (n) is the actual output η is the learning rate n is the number of iterations)