hm9

关于matlab编程的基本规则和说明，作者是richard johnson(basic rules and description about how to program in matlab,the author is richard johnson)

最优分割的基本思路是给定一个数据序列和分层数，通过搜索所有可能的划分方案，找到段内离差平方和的总和最小的一种方案作为最终划分方案。这里给出最优分割法的Matlab源程序(The basic idea is optimal partition for a given data sequence and hierarchical number divided by searching all possible solutions to find the segment and the sum of the squared deviations smallest division of a scheme as a final solution. This gives the optimal segmentation Matlab source)

利用信道容量迭代算法，求下列转移概率矩阵所对应信道的容量和达到容量时的输入概率(Use of channel capacity iterative algorithm, and the following transition probability matrix corresponding to the probability of reaching capacity when capacity input channel and)

小波分析的基本理论，内容全面，包括matlab实现小波分析的实例和源代码，适合学习小波分析和研究。(The basic theory of wavelet analysis is comprehensive, including examples and source code matlab wavelet analysis, the suitable learning wavelet analysis and research.)

有限元 matlab 程序-----二维圆柱体的散射 (Finite element matlab program----- scattering of two-dimensional cylinder)

ofdm的matlab仿真程序，对学习ofdm技术的人员来说比较实用(ofdm matlab simulation program, to learn ofdm technology officer for more practical)

MATLAB program of v/f control of ac machine

雷达旁瓣对消器，应用于雷达抗有源旁瓣干扰。(Radar sidelobe canceller, used anti-active radar sidelobe interference.)

一共包括LMS,自适应陷波器，自适应RLS自适应算法等5种算法的MATLAB程序(Total, including LMS, adaptive notch filter, adaptive RLS adaptive algorithm, such as five kinds of procedures MATLAB algorithm)

matlab 关于association rule 的自己写的函数，有3个文件, association.m：h = association(m, i, j) i=>j, m是数据，h是support和confidence，该函数只适用于单个数据 ass_item: h=ass_itset(m, a, b) 同上，但是可用于多个数据（m为数组） assrule: h = assrule(m, threshold1, threshold2) 该函数用于classification， 得到规则，threshold1为要求的support,threshold2为要求的confidence，h 则为符合要求的规则及其support和confidence，前2列为规则，后2列为其support和confidence (matlab on the association rule to write functions, there are 3 files, association.m: h = association (m, i, j) i => j, m is the data, h is the support and confidence, this function applies only to a single Data ass_item: h = ass_itset (m, a, b) it is the same as above, but it can be used for multiple data (m can be matrix) assrule: h = assrule (m, threshold1, threshold2) the function used for classification,get the rules, threshold1 is the require of support, threshold2 is the required of confidence, h is the rules and their support and confidence, the former two columns as a rule, the latter two columns as one of its support and confidence)