基于K-means的负荷聚类

ART2网络的MATLAB程序，自己编写的。最近一系列的文章都是用这个作的实验。在科学网可以找到本人博客下载相关论文(ART2 network MATLAB procedures, I have written. The recent series of article are made using this experiment. Net can be found at science blog I download related thesis)

Computes the Kalman gain and the stationary covariance matrix using the Kalman filter of a linear forward looking model.

说明：  伪随机序列产生器－filtered 代进位反馈移位寄存器，matlab 原代码。(Pseudo-random sequence generator-filtered on behalf of binary feedback shift register, matlab source code.)

说明：  图象压缩方面的论文pdf格式 可用于毕业设计方面的工作(Image compression pdf format papers for school design work)

sift algorithm for detection of object even in different orientations

基于DCT变换的图像压缩的基本原理及其实现步骤。采用DCT变换压缩的依据。图像压缩过程的最后步骤是编码量化了的图像。在此,我采用的是Huffman编码方法。并使用MATLAB7.0实现DCT图像压缩的仿真。(DCT based on the image compression)

这个包里面包括了SIFT特征点检测及匹配的Matlab代码，不是那种编译好的exe文件，对于该算法的理解有一定的帮助，另外包含一些测试的图片，有小图和大图，测试大图的时候速度可能有些慢，还有待进一步的优化。不过对于算法学习绰绰有余了。

运用粒子群优化算法对ARMA(p,q)模型的阶数进行优化是可行的，且利用优化后的ARMA模型对航空发电机状态趋势进行分析的结果十分准确，与原始数据所表现出的航空发电机状态变化趋势基本一致。经计算，此时分析结果的平均相对误差较小，仅为1.38 ，与优化前构建的ARMA模型相比，平均相对误差降低0.23个百分点，优化效果明显。(Using particle swarm optimization algorithm to optimize the order of the ARMA (p, q) model is feasible, and the use of the results of the ARMA model optimized air generator status trends analysis is very accurate, and the raw data showed aviation the generator status consistent trend. Calculated analysis of the results, the average relative error is small, only 1.38 , compared with the the ARMA model built before optimization, average relative error is reduced by 0.23 percentage points, to optimize the effect is obvious.)

晨宇思远博客的代码------文件中代码实现小波图像融合，对低频和高频系数采用不同准则，有详细说明文件中代码实现小波图像融合，对低频和高频系数采用不同准则，有详细说明( morning yu siyuan blog code----------- file code implements wavelet image fusion, the low frequency and high frequency coefficient using different criteria, there are detailed description file code implements wavelet image fusion, the low frequency and high frequency coefficient using different criteria, there are elaborate)

在matlab中读取点云数据stl文件并在界面显示(Read the point cloud data STL file in MATLAB and display it on the interface.)