detection

ecoli ten fold cross validation set (part 1)

用于分类的主判别变量算法(principal discriminant variate algorithm) ( principal discriminant variate algorithm for two classes, x1 and x2. x1 is the training samples in the 1st class with each column being the spectrum characterizing the corresponding sample. x2 is the training samples in the 2nd class with each column being the spectrum characterizing the corresponding sample. lamda is the penalty weight defining the balance between FLDA and PCA. dvnum is the number of principal discriminant variates to be computed. A defaul value for dvnum is 2. dv is the principal discriminant variates. scores is the matrix with each row being the scores of the samples on the corresponding principal discriminant variate. xmean is the mean spectra of the sample, which was used for mean-centering the data in training and prediction. )

粗糙集属性约简相关论文，感觉不错，用来分享一下(Rough set attribute reduction related papers, I feel good, to share)

图片识别，能够识别图片的信息然后摘取处理(Image recognition)

this is MLP project in Neural Network that have userinterface. run GUI.m to execute project

说明：  求正弦信号和余弦信号的傅里叶变化，以及图形之间的转变(Sine signals and cosine signals request the Fourier transform, and the graphics change between)

车牌分割技术，主要应用于对车牌的识别，使用matlab编写。(car discrete)

递推最小二乘法和递推阻尼最小二乘法仿真，系统模型如下： y(t) - 1.6 * y(t - 1) + 0.7 *y (t - 2) = u(t - 1) + 5.5 * u(t – 2) + e(t)，试用递推最小二乘法和递推阻尼最小二乘法进行参数识别，并进行比较 (Damped recursive least squares method and recursive least squares method simulation, the system model is as follows: y (t)- 1.6* y (t- 1)+ 0.7* y (t- 2) = u (t- 1)+ 5.5* u (t- 2)+ e (t), the trial damped recursive least squares method and recursive least squares method for parameter identification, and comparison)

最新的定步长LMS自适应滤波算法，对自适应算法有很大帮助(The latest fixed step size LMS adaptive filtering algorithm)

一个GM(1,1)模型的程序,添加到matlab目录中即可直接调用.附有一个关于灰色模型的简述文档,可对GM(1,1)原理有一个初始的了解.(A GM (1,1) model of the process, added to the matlab directory can be called directly. Accompanied by a brief description of the gray model on the document, can GM (1,1) principle have an initial understanding.)