SA_TSP

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)

利用边界追踪法对图像进行轮廓提取，此函数文件得到的是边界点对应位置索引矩阵(Boundary tracking method using the image contour extraction, this function is to get the file location of the boundary points corresponding to the index matrix)

This book relates to Kalman filter theory

一个本科毕业设计，关键是有源码。内容为ofdm系统仿真，包括设计方案，仿真参数设置等。(A graduation project, the key is a source. Content ofdm system simulation, including the design, simulation and parameter setting.)

经验模态分解EMD-希尔伯特-黄变换的HHT，的MATLAB，用户图形界面。(EMD-HHT )

本书中重点介绍了Matlab6.中图像处理的内容(matlab for image processing)

SNAMP代码，供大家学习研究设计使用，希望对你有帮助(SNAMP code for them to learn from research design to use, want to help you)

史密斯圆图的应用, 包括阻抗匹配，计算方法等(Smith chart applications, including impedance matching, calculation methods)

数字信号处理中的时域抽样定理，作为初学者自己编写的，绝对正确，有详细的推导(Digital signal processing in the time domain sampling theorem, as a beginner I have written, absolutely right, there is a detailed derivation)

本设计采用了一种基于三参数组合的方法对语音信号进行了清/浊音判决。在传统两参数（短时能量和过零率）算法基础上增加了自相关函数作为判决参数，减少了清/浊音信号误判率。(This design uses a combination of methods based on three parameters of the voice signals of the Qing/voiced sentence. In the traditional two parameters (short-term energy and zero crossing rate) algorithm based on the increase since the correlation function as a decision parameter, reducing the clearance/voiced signal false positives.)