calculateuserposition

GM（1，1）源代码，计算原始数据的残差、关联度、小误差概率等。同时画出原始数据和预测数据对比图(GM (1,1) source code to calculate the raw data residuals, correlation, and so a small error probability. At the same time draw the raw data and forecast data comparison chart)

Image Processing Toolbox

说明：  求解分数阶微分方程的Runge_Kutta方法Fractional Differential Equations of Runge_Kutta method(Fractional Differential Equations of Runge_Kutta method)

leach 源码 c++编写名字怎么要这么长啊 (leach source c++ how to write the name so long ah)

实现9/7小波正反变换 注1： 采用标准正交方法，对行列采用不同矩阵（和matlab里不同） 注2： 为了保证正交，所有边界处理，全部采用循环处理 注3： 正交性验证，将单位阵带入函数，输出仍是单位阵（matlab不具有此性质） 注4： 此程序是矩阵实现，所以图像水平分量和垂直分量估计被交换位置 注5： 此程序实现的是类小波（wavelet-like）变换，是介于小波包变换与小波变换之间的变换 注6： 此程序每层变换相对原图像矩阵，产生的矩阵都是正交阵，这和小波包一致 注7： 但小波变换每层产生的矩阵，是相对每个待分解子块的正交矩阵，而不是原图像的正交矩阵 注8： 且小波变换产生的正交矩阵维数，随分解层数2分减少 注9: 提升系数可以在MATLAB7.0以上版本，用liftwave( 9.7 )获取，这里直接给出，考虑兼容性 注10：由于MATLAB数组下标从1开始，所以注意奇偶序列的变化 注11：d为对偶上升，即预测；p为原上升，即更新(Note 1: The standard orthogonal method, the ranks of the different matrices (and matlab where different) Note 2: In order to ensure orthogonality, all boundary treatment, all with recycling Note 3: Orthogonal validation, the identity matrix into the function, the output is still the unit matrix (matlab does not have this property) Note 4: This program is a matrix to achieve, so the image horizontal and vertical components is estimated to be swapped Note 5: This class implements the wavelet (wavelet-like) transformation, is between the wavelet packet transform and wavelet transform between Note 6: This procedure transforms each image relative to the original matrix, the resulting matrix are orthogonal matrix, consistent with this and wavelet packet Note 7: But each generation wavelet transform matrix, is to be broken down for each sub-block relative to the orthogonal matrix, rather than the original image of the orthogonal matrix Note 8: Wavelet transf)

SVM主程序,专门用来调用SVM程序的,主要是先建模再验证，得出较好的分类器。(SVMmain program，for using SVM to classify)

《生物医学数字信号处理》,生物医学中ECG信号的处理，强调实时处理算法。(“Biomedical Digital Signal Processing”,Biomedical ECG signal processing, emphasizing the real-time processing algorithms.)

CLAHE的matlab实现算法，经过测试可用。(CLAHE the matlab algorithm, tested available.)

模拟信号线性调制系统设计,Simulink的使用方法，能熟练运用该软件设计并仿真通信系统dsb,vsb,ssb(Linear analog signal modulation system design, Simulink use can skilled use of the software design and simulation of communication systems dsb, vsb, ssb)

In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets. (In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets. )