matlab_source_code

o here p q r s t of the electro cardia program is being explained

计算电磁学 二维PML程序 采用MATLAB编写(PML-dimensional computational electromagnetics programs written using MATLAB)

The matlab program describes the Jacobian function

说明：  神经网络控制源程序，徐丽娜神经网络配套程序(Neural network control source program, xu ln neural network supporting program)

说明：  现代信号处理中对基本的ESPRIT的一种改进算法的程序，并利用算法进行了信号频率的统计仿真实验！(Modern signal processing of basic ESPRIT An Improved Algorithm for the procedures and the use of algorithms of statistical signal frequency simulation!)

贝叶斯分类器，首先生成3000个高斯分布的点，1000个点做训练集，2000个点做测试集。先运行data_generator.m自动生成两个集盒，再运行bayes_classifier.m进行分类(Bayesian classifier, the first generation 3000 Gaussian distribution of points, 1000 points to do the training set, 2000 points to do the test set. Automatically generated the first two sets running data_generator.m box, and then run bayes_classifier.m classification)

this program is about airs algorithm iti role is bout classification of many vectors of data

说明：  关于VC++和Matlab混合编程的一本PDF格式的书籍。(On the VC++ and Matlab programming mixed a PDF format of the book.)

说明：  本程序利用Logistic映射产生混沌序列，并比较其游程特性(This program makes use of the Logistic map to produce a chaotic sequence, and compare its run-length features)

EM算法在神经网络中的应用，可以用来进行视频数据分类。(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.)