Data

用matlab实现的分类算法，非常经典的，功能齐全(Using matlab realize the classification algorithm, a very classic, full-featured)

基于FCM的图像分割算法，先对图像平滑，然后利用FCM对像素聚类，实现对图像的分割。(A methord based on FCM for image segmenting,firstly,using filter to smooth the image and the use the rule of FCM clustering pix finshing image segmention)

The Art of Electronic 电子学（第二版 吴利民译）很经典的电子基础书籍(The Art of Electronic)

基于matlab的超像素图像分割算法实现代码，将图像分割为小块表示(Matlab-based super-pixel image segmentation algorithm code, the image is divided into small representation)

加氢装置分馏塔相关质量数据，用matlab建模用于预测(The hydrogenation unit fractionator quality data, matlab modeling used to predict)

说明：  一种FxLMS自适应滤波算法 LMS自适应滤波算法RLS自适应滤波算法变换域自适应滤波算法仿射投影算法共扼梯度算法基于子带分解的自适应滤波算法基于QR分解的自适应滤波算法算法性能评价变步长的自适应滤波算法时变系统跟踪速度与收敛精度方面对算法调整步长因子u的矛盾,但变步长中的其它参数的选取还需实验来确定,应用起来不太方便。对RLS算法的各种改进,其目的均是保留RLS算法收敛速度快的特点而降低其计算复杂性。变换域类算法亦是想通过作某些正交变换使输入信号自相关矩阵的特征值发散程度变小,提高收敛速度。而仿射投影算法的性能介于LMS算法和RLS算法之间。共扼梯度自适应滤波算法的提出是为了降低RLS类算法的复杂性和克服某些快速RLS算法存在的数值稳定性问题。信号的子带分解能降低输入信号的自相关矩阵的特征值发散程度,从而加快自适应滤波算法的收敛速度,同时便于并行处理,带来了一定的灵活性。(A FXLMS adaptive filtering algorithm)

简明卡尔曼滤波介绍，适用于初学者，很容易理解。(Kalman filter concise introduction for beginners, it is easier to understand.)

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. (This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. )

colea程序，处理语音信号的M函数集合，对于学习语音处理有很大帮助(colea procedures to deal with M-function voice signal a collection of voice processing for the study of great help)

Tranformasi wavelet merupakan perbaikan dari transformasi Fourier. Transformasi Fourier hanya dapat menangkap informasi apakahsuatu sinyal memiliki frekuensi tertentu ataukah tidak, tapi tidak dapat menangkap dimana frekuensi itu terjadi. Jika Transformasi Fourier hanya memberikan informasi tentang frekuensisuatu sinyal, maka transformasi wavelet memberikan informasi tentang kombinasi skala dan frekuensi