zantaiwending

该程序，可以用于计算金融证券期权产品的三项树定价估值过程。(This matlab code can be used to caculate the options price by estimate the trinomial processes.)

The code decribes a gas-solid model to predict the conversion variation with various affecting parameters.Very useful for Matlab rookies

cx_3 lpp source code with many examples

This file calculates BER of BPSK in AWGN for various values of Eb/N0 and then compares simulated BER with theoretical BER

MPEG-2 System specification

matlab中关于函数的经典、简单说明，看懂了这个函数例子你就明白matlab中的函数使用精髓。(The classic description about matlab function)

一个比较完善的Matlab画图,较为有用并且符合实际(Matlab 3D figure)

详细介绍了matlab在矩阵运算、数据分析和统计、线性方程系统、特征值和特征向量、曲线拟合、微积分等方面的应用。(Matlab described in detail in the matrix operations, data analysis, and statistics, systems of linear equations, eigenvalue and eigenvector, curve fitting, calculus and other applications.)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)

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.)