Kmeans

pmsm的矢量控制模型，喜欢的可以下载看看，不完善的地方欢迎大家讨论

我们的目的是将一个分数拆分成若干个分子是1分母是正整数的分数之和的形式。例如2/3可以拆分成1/2+1/6的形式。这里不考虑顺序，比如1/6+1/2和1/2+1/6相同。 输入p, q, a, n, 其中p/q为分数，对这个分数进行拆分，要求拆分的分数个数不能多于n个，所有分母的乘积不能大于a。求有多少种拆分方法? 输入：最多200组数据。在每行中都有四个数分别表示p, q, a, n，之间以空格隔开，并且满足p,q<=800，a<=12000，n<=7，最后以四个0做为输入的结束。 输入示例: 2 3 120 3 2 3 300 3 0 0 0 0 输出示例: 4 7 (Our aim is to split a fraction into a number of elements is a denominator is a positive integer fraction of the sum of form. For example, 2/3 can be split into 1/2+1/6 form. Not considered here, the order, such as 1/6+1/2 and 1/2+1/6 of the same. Input p, q, a, n, where p/q as a fraction of this split scores require no more than the number of split points n-all the product of the denominator can not be larger than a. How many kinds of demand split method? Input: Up to 200 sets of data. In each line has four numbers indicate the p, q, a, n, separated by a space between, and meet the p, q < = 800, a < = 12000, n < = 7, the end, four 0 as the input end. Input Example: 2,312,032,330,030,000 sample output: 47)

MATLAB 数字信号处理与应用 清华大学出版社 程序(MATLAB application of digital signal processing and Tsinghua Press)

matlab从入门到精通 学习资料 很全面的 (matlab entry to the proficient very comprehensive learning materials)

针对宽带信号的的LFM（线性调频信号）信号的simulink仿真，包括频谱图等(I have just taken the job of the matlab simulation for the LFM signal.)

此算法是基于PN序列的经典的定时同步算法，能直接出图，望对大家有用(This algorithm is based on the classic PN sequence timing synchronization algorithms, directly out of maps, useful look on the U.S.)

数学建模2008年D题.该文件是2008年全国研究生数学建模D题的求解软件包。(Mathematical Modeling 2008 D. This document is the solving package of 2008 National Graduate D mathematical modeling problem.)

由语言编和应用编两部分组成，语言编介绍MATLAB语言的基本语法，既便于自学，应用编给出80个实例(it composed by language and application, language part describes the basic syntax of the MATLAB ,and application part have 80 examples .both for self_learning)

说明：  RS码的C实现，通过mex接口，在MATLAB中创建的LTE下行链路中测试，可跑出误码率曲线。(RS code is implemented in C, by mex interface, LTE downlink created in MATLAB test can be run out of BER curves.)

使用高斯模型对威斯康辛州大学医学院长期乳腺癌数据进行了贝叶斯模式识别。识别率为95以上，可以作为模式识别的重要案例。(Gaussian model using the University of Wisconsin School of Medicine conducted a long-term breast cancer data Bayesian pattern recognition. Recognition rate is above 95 can be regarded as an important case of pattern recognition.)