digitalTransmission

：介绍了扩展卡尔曼滤波算法和无迹变换(unscented transformation，UT)算法，并对扩展卡尔曼滤波算法(EKF)和无 迹卡尔曼滤波算法(UKF)进行比较，阐明了UKF优于EKF。在此基础上，提出了一种基于Unscented变换(uT)的高斯和滤 波算法，该算法首先通过合并准则得到适当个数的混合高斯模型，逼近系统中非高斯噪声的概率密度(: Introduction of the extended Kalman filter algorithm and unscented transform (unscented transformation, UT) algorithm, the extended Kalman filter algorithm (EKF) and unscented Kalman filter (UKF) for comparison to clarify the UKF is superior to EKF. On this basis, we propose an approach based on Unscented Transform (uT) and the Gaussian filtering algorithm, which first of all, by merging the appropriate number of criteria to be a mixture of Gaussian model, which was close to the system of the Central African Gaussian noise probability density)

说明：  本程序用pca，kpca,svm,pls,fisher，贝叶斯实现cstr和csth过程的故障检诊断，诊断率为百分之九十九，故障识别率为百分之七十四(The program use pca, kpca, SVM, PLS, fisher, bayesian realize CSTR and CSTH process for diagnosis fault diagnosis rate is ninety-nine percent, and the fault recognition rate is seventy-four percent )

弹性波在饱和多孔介质中传播的BISQ模型MATLAB源码。(Elastic wave propagation in saturated porous media BISQ model MATLAB source. )

（压缩包里一共有5个代码） pca+lda+粗糙集+模糊神经网络 saveORLimage.m将ORL人脸库分为测试集ptest和训练集pstudy存为imagedata.mat 1.savelda.m将人脸库先进行pca降维，再用lda进行特征提取，得到新的测试集ldatest和训练集ldastudy存为imageldadata.mat 2.对ldastudy进行离散化(discretimage.m)，得到离散化矩阵disdata,存入到imagedisdata.mat 3.将disdata组成决策表(savers.m)，通过对disdata的条件属性进行约简，得到其一个约简，组成新的测试集rstest和训练集rsstudy存为imagersdata.mat 4.对rsstudy进行模糊神经网络训练(savecul.m)，对模糊神经网络的参数进行调整学习将其存入culdata.mat 5.用runfnn.m对rstest进行测试得到其识别率 savem.m和cm.m是用最小距离分类器对训练集和测试集进行分类.(pca+ lda+ Rough Set+ fuzzy neural network saveORLimage.m will ORL face database is divided into test set and training set ptest for pstudy keep imagedata.mat Treasury will face 1.savelda.m first dimensionality reduction pca, lda used feature extraction, a new test set and training set ldatest for ldastudy keep imageldadata.mat 2. Ldastudy carried out on the discretization (discretimage.m), to be discrete matrix of disdata, deposited to imagedisdata.mat 3. Disdata the composition of the decision table (savers.m), the conditions on the attributes disdata about Jane, has been one of its reduction to form the new test set and training set rstest for rsstudy keep imagersdata.mat 4. Rsstudy training fuzzy neural network (savecul.m), on the parameters of fuzzy neural network to learn to adjust their deposit culdata.mat 5. Rstest used to test for runfnn.m by its recognition rate cm.m is savem.m and minimum distance classifier on the training set and test set classificati)

说明：  LMS算法的仿真程序，能直观的看出该算法的优点(LMS)

与潘左击鼠标拖动，绘制一个右键拖动zoombox，并检视双击所有数据。这也保留任何现有的WindowButtonXFcn这个数字已，以及任何轴UIContextMenus。 我写了实时数据采集这一点，因为linkaxes套XLimMode至“手动“ -这意味着将不会自动调整轴的新数据。 panzoom（figureHandle，axesHandles） 添加到这个数字，只能到指定的坐标轴功能的处理。 panzoom（figureHandle，axesHandles，XLink的） panzoom（figureHandle，axesHandles，XLink的，雁联） 假设你有四个轴处理：1，2，3，4。你可以连接所有的X轴一起：XLink的= [1，2，3，4]，或者你可以对链接在一个单元格数组中：XLink的={[1，2][3，4]}。这同样适用于雁联。 XLink的或雁联也可以为空。(Pan with left click mouse drag, draw a zoombox with right click drag, and view all data with double-click. This also retains any existing WindowButtonXFcn the figure has, as well as any axes UIContextMenus. I wrote this for live data capture, as linkaxes sets XLimMode to "manual"- which means the axes wouldn t automatically resize for new data. panzoom(figureHandle, axesHandles) Adds the functionality to that figure, ONLY to the axes handles specified. panzoom(figureHandle, axesHandles, xLink) panzoom(figureHandle, axesHandles, xLink, yLink) Assuming you have four axes handles: 1, 2, 3, 4. You could link all of the x axes together: xLink = [1, 2, 3, 4], or you could link them in pairs in a cell array: xLink = {[1, 2], [3, 4]}. The same applies for yLink. xLink or yLink can also be empty.)

Source code for MD5 in matlab

The function alphaBetaFilter implements a generic algorithm for an alpha-beta filter that is a linear state estimation for position and velocity given an observed data. It acts like a smoothing. Also closely related to Kalman filters and to linear state observers used in control theory. Its principal advantage is that it does not require a detailed system model.

基于matlab语言的动态规划程序，可以很好的解决初学者所需的问题，通俗易懂(Matlab program based on dynamic programming language, can solve the problem needed to beginners, easy to understand)

阵列信号处理领域的基于Lcmv准则的波束形成方法，对初学者有用。(Array signal processing based on the criteria Lcmv beamforming method, useful for beginners.)