CART

分析剖析器，深入的综述数据科学家和他们的工作是应用数据科学的方法到专业社区的结果。(Analyzing the Analyzers: An Introspective Survey of Data Scientists and their Work is the result of applying the methods of data science to our own professional community. )

永磁直驱式风力发电系统的simulink最终整体的仿真搭建 最终实现的结果很好(Permanent magnet direct drive wind power generation system simulink simulation structures and ultimately the end result of a good overall)

音乐分类的几种方法，解决传统音乐风格划分的模糊性。(Music classification are several ways to solve the traditional music style by vagueness.)

在MATLAB生成的FIG中进行局部放大，(FIG generated in MATLAB in the enlarged)

信息类专业信号与系统课程实验报告,包含卷积,滤波器分析等.(Information Specialty Signals and Systems course experiment report, including convolution, filter analysis.)

Matlab 程序实现现代功率谱估计。包含自相关法，burg法，改进协方差法。(A m file using modern spectrum estimation ,including Autocorrelation metohd and Burg method, and modified Covariance method.)

地震波信号处理，傅氏变换，震相读取，文件内有使用说明(seismic signal process,fft,phase check)

说明：  CA算法可以将数量型属性划分成若干个优化的区间，它综合了分层聚类于划分聚类的优点，对于给定的不同的初始类个数，CA算法能随着迭代过程的不断进展改变类的数目，一些竞争力差的类即类的基数小于给定阙值的类将在迭代过程中不断消失，最终得到能够有效体现数据实际分布情况的优化聚类个数。(CA algorithm for quantitative attributes can be divided into a number of the interval optimization, hierarchical clustering which combines the advantages of clustering in the division, for a given number of different types of initial, CA algorithm as the iterative process the continuous progress of change in the number of categories, some categories of poor competitiveness of the base type that is less than the value of a given category Que iterative process will continue to disappear and eventually be able to effectively reflect the actual distribution of data to optimize the number of clustering.)

采用信号包络和差分方式进行信号模式识别，通过变换产生时域解析信号SKH ，即信号的复包络，得到零中心归一化瞬时绝对值的标准偏差。 (Differential use of signal envelope and the way the signal pattern recognition, transformation generated by time domain analytic signal SKH, the signal complex envelope, zero-center normalized standard deviation of the instantaneous absolute value.)

fft分析：方法简单实用，希望能够帮助大家，大家可以参考一下(fft Analysis：very easy！)