matlabADC

MATLAB应用程序实例300个，包括图形应用篇、界面设计篇、图形处理篇、数值分析篇，相当经典！(they are Examples of MATLAB applications 300, including the application of article graphics, interface design articles, graphics processing papers, numerical analysis of articles, they are quite classic!)

变压器matlab仿真，用于实现基本的变压器操作和应用(Transformers matlab simulation, used to implement the basic operation and application of transformer)

MATLAB/simulink2013b最新版新用户说明书和老用户使用说明。(MATLAB/simulink2013b latest version of the new users and old users manual instructions.)

说明：  标准图像，包含多幅用于matlab的标准图像(The standard image, contains many standard for matlab image)

实现ID3算法，在结果中以树表示出来。决策树是对数据进行分类，以此达到预测的目的。该决策树方法先根据训练集数据形成决策树，如果该树不能对所有对象给出正确的分类，那么选择一些例外加入到训练集数据中，重复该过程一直到形成正确的决策集。(ID3 algorithm to achieve, in the results that come out to the tree. Decision tree is to classify the data, thus achieving the purpose of prediction. The decision tree training set of data according to the formation of the first decision tree, if the tree can not give the correct classification of all objects, then select a number of exceptions to the training set data, repeat the process until the correct decision set.)

code for full estimation of disparity map from two right and left image

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)

希尔伯特黄变换（HHT）变换，在众多领域都有应用，适合初学者学习(Hilbert Huang Transform (HHT) transformation has applications in many fields, suitable for beginners to learn)

定制神经网络的实现——神经网络的个性化建模与仿真(Artificial Neural Networks matlab)

Simulation of Single phase halfwave controlled rectifier