BasicAntColonAlgorithmmatlabforTSP

J. Han and M. Kamber. Data Mining: Concepts and Techniques.

简单数字调制编程，方便初学者学习 简单数字调制编程，方便初学者学习(Simple digital modulation program, easy for beginners to learn simple programming of digital modulation, easy for beginners to learn)

时间序列数组的创立和数据文件读取(金融工具箱/Financial Toolbox/fints/ascii2fts)(The creation of an array of time series and data files to read (the financial toolbox/Financial Toolbox/fints/ascii2fts))

image quality measurement

统的联合概率数据关联算法(IPDA)是在密集杂波环境下的一种良好的多目标跟踪算法， 但它是针对单传感嚣对多目标跟踪的情况下使用，不能直接用于多传感器对多目标的跟踪。针对这 一问题，文中提出了一种适用于多传感器多目标跟踪的JPDA算法，它以极大似然估计完成对来自 多传感器的测量集合进行同源最优分划，然后采用JPDA方法对多目标进行跟踪。经过理论分析和 仿真试验，证明了该方法能有效地进行多传感器多目标的跟踪，且具有算法简单、跟踪精度高、附 加的计算量小等优点。(Abstract)m JointProbabilisticDataAssociation(JPDA)solves single sensormultitatgcttrackingin ClUtter,butitc锄not bc used directly in mnitisensor multitarget tracking．喇8 paper presents a method to implement multi—sensor multi—target tracking by combining maximum likelihood estimation wim JPDA The maximum Iikelihood estimation is used to classify the same source observations at one time into the same set,and血en JPDA implements multi—target uacking after the position fitsed．The theory analysis and computer simulation show that this algorithm may achieve multi—sensor multi—target tracking perfectly with low calculation load added and higher precision 。)

主分量分析是一种基本的多维数据描述方法，是一种能使多维数据按照实际研究的需要更好地描述出来的变换方法。(Principal component analysis is a basic description of the method of multidimensional data is a multidimensional data in accordance with the needs of the actual research to better describe the transformation method can make.)

该算法是lte上行链路的方针，对于初学者有一定的帮助。(The algorithm is LTE Uplink policy, have some help for beginners.)

考虑微分方程在如下步长分布采用向前Eular、向后Eular，梯形公式、改进Eular和四阶龙格库塔方法计算近似值(procedure of approximate error using matlab)

If the used buffer size is smaller than the actual buffer size, regenerate the lookup table and write the new table to the file.

The first scheme is based on the spatial locality of feature vectors corresponding to similar images. Learning is effected by modifying the query vector to incorporate the positive examples. The second scheme is based on “distorting” our view of the feature space. An new similarity distance between an image and the query is learned from the relevance feedback.