myPIDshifj

说明：  convert binary numbers into decimal numbers and anthor convert decimal into binary

此应用程序不仅表明了基本的使用Microsoft基础类，而且也是一个起点书面申请。(This application not only demonstrates the basic use of Microsoft Foundation Classes, but also a starting point for an application in writing.)

说明：  QCldpc编码的基础论文，包含期刊和会议的论文，还挺好的(QCldpc coding in basic academic，Including journal and conference papers, pretty good)

结构动力分析MATALB源程序, 比较经典，希望大家喜欢，呵呵(Structural dynamic analysis MATALB source, more classic, hope you like it, Oh)

希望大家共同探讨问题，共同进步，祝大家学业有成！(U.S. to discuss the issue of hope and common progress, I wish everyone with achieving!)

matlab 遗传算法工具箱 GATBX (matlab genetic algorithm toolbox)

T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm ) 训练次数为1000，训练目标为0.01，学习速率为0.1 net.trainParam.epochs=1000 net.trainParam.goal=0.01 LP.lr=0.1 net = train(net,P,T) 测试数据，和训练数据不一致 P_test=[0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 (T = [1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] ' of the maximum and minimum input vector threshold = [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net = newff (threshold, [31 3], {' tansig' , ' logsig' }, ' trainlm' ) training times for the 1000 target of 0.01 training, learning rate of 0.1 net.trainParam.epochs = 1000 net. trainParam.goal = 0.01 LP.lr = 0.1 net = train (net, P, T) test data, and training data inconsistencies P_test = [0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 )

MatrixVB 使用手册:介绍在VB中调用matlab函数的控件的使用方法及例子。在VB中使用强大的matlab运算工具！(MatrixVB User’s Guide)

说明：  用于人工智能分类算法仿真的数据集 UCI数据集 已经过集成(Simulation of artificial intelligence classification algorithm for data sets UCI data sets have been integrated)

LS-SVM Leave-One-Out Cross-Validation Demo G. C. Cawley, "Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs", Proceedings of the International Joint Conference on Neural Networks (IJCNN-2006), pages 1661-1668, Vancouver, BC, Canada, July 16-21 2006. (pdf)theoval.cmp.uea.ac.uk/~gcc/matlab/