chuandiduizhun

说明：  基于奇异谱的齿轮箱振动信号分析，含主程序及熵值函数。(The vibration signal analysis of Gearbox Based on singular spectrum includes main program and entropy function.)

高斯法，高斯选主元法，LU分解法求解病态问题（非调用）(Gaussian, Gauss pivoting method, the LU decomposition method for solving ill-posed problems)

随机数发生器 此功能会产生连续的值（功法的文章中讨论的五个分布随机分布，指数，对数正态分布，拉伸指数，截止功法）。使用信息包含在文件类型的帮助randht在MATLAB提示符更多信息。 幂律分布拟合 这项功能同时实现的离散和连续的最大似然估计拟合幂律分布的数据，以及善良的合适的方法来估计缩放区域较低的截止。使用信息包含在文件类型的帮助plfit在MATLAB提示符更多信息。(This function is a random number generator will produce discrete values ​ ​ (power law discussed in five articles distributed random distribution, exponential, lognormal, stretching index cutoff power law). The use of information contained in the file type of help randht in MATLAB prompt for more information. Power-law distribution fitting This function implements both discrete and continuous maximum likelihood estimation fitting power-law distribution of data, and kind suitable method to estimate the scaling region lower cutoff. The use of information contained in the file type of help plfit in MATLAB prompt for more information.)

难得的5篇关于神经网络进行视频压缩的资料，全网络仅有的5篇。(A rare 5 articles about neural network video compression data, the whole network only 5 articles.)

《MATLAB GUI设计学习手记(第2版)》在第1版的基础上，完善了全书知识结构，突出了gui设计重点，对读者经常遇到的38个问题作了透彻的解答，并提炼出13个专题作了详尽的介绍。(MATLAB GUI design Learning Notes (2nd Edition) " on the basis of the first edition, improve the structure of the book knowledge, highlighting the GUI design focus, made a thorough answers to 38 questions that readers often encountered and extract 13, made a detailed introduction to the topic.)

SVM典型三分类程序 自带数据特征 可完美运行(SVM classification program comes with three typical characteristics of the data can be perfect run)

Programming Manual for MATLAB

匀速模型目标跟踪方法，并给出相应的滤波分析，能运行，欢迎下载(Uniform model for target tracking method of filtering the corresponding analysis, will be able to run, please download)

Optical Charactor Recognition

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 )