Liquid-the-controller-output

核直接线性判别方法（kdda），主要用于人脸识别，模式分类。(nuclear direct linear discriminant method (kdda), mainly used for face recognition, pattern classification.)

matlab code for dynamic time warping

说明：  基于神经网络的预测方法，可用于光伏预测，拟合较好(BP forecast based on Matlab)

采用matlab，实现了gabor滤波的核心功能(gabor filter is very famous and really useful for texture feature extraction and image retrivel. attached code is wrriten well using matlab. More detail can be referenced in the paper <texture features for browsing and retrieval of image data>)

说明：  显式差分法解传热方程的matlab脚步文件，并作图，内附说明，并保证解的稳定性。(this m file is using explicit FDT to solve one dimension heat transport PDE function)

说明：  使用Matlab编写的逆谐波程序。该程序对于不同的参数进行了实验。当图像噪声不同时，选用不同的参数。参数的正确选择很重要。本程序给出了不同噪声比较适合的参数选择(The use of Matlab procedures for the preparation of the inverse harmonic. The program parameters for different experiments. When the image noise is not at the same time, choosing different parameters. Correct choice of parameters is very important. This procedure gives more suitable for different noise parameters)

实时处理声音降调 matlab源代码 短时傅里叶变换(Real-Time Speech Pitch Shifting)

LIBRARY FOR SERVO FOR ARDUINO

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)

DWT and zadoff-chu000000