matlab---dpsk

说明：  实现数字滤波（刚开始接触不知道分类对不对）请见谅(Digital filter (do not know the beginning of exposure classification is correct or not), please forgive me)

matlab code for Grid connected PV system with MPPT

DBSCAN算法实现，基于增量聚类算法的实现(DBSCAN algorithm, incremental clustering algorithm based on density of the source code)

OCT信号模拟 很理想化 但对初学者帮助较大(OCT signal simulation is ideal for beginners but for more help)

pre_distrion源程序，smilink源程序，可以对初学者有一定的帮助。(pre_distrion source, smilink source, can be helpful for beginners.)

介绍了对信号小波包去噪和求功率谱密度的程序(Introduced the wavelet packet denoising and seek the power spectral density of the program)

对图像提取相应的特征点，并对特征点进行精简与描述，找到128维的描述子，并与另一幅图像的描述子进行比较，得到图像的匹配(Extract the corresponding feature point of the image, and the feature point streamline with the description, to find 128-dimensional descriptor, and compares with a description of the image sub-image matching)

This is a useful book for beginners in MATLAB

说明：  网上牛人matlab实现 emd算法,可以下载尝试一下的(matlab'emd eemd code)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)