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该程序为matlab进行傅立叶变换的快书算法FFT，其中还包括了傅立叶变换的其他几种快速算法．(The program carried out for the matlab book fast Fourier transform algorithm FFT, which also includes several other Fourier Transform fast algorithm.)

说明：  自适应算法中possion过程解的程序代码，紧接着上一篇上载内容(possion solves)

信号FFT的MATLAB的编程，对于初次使用MATLAB的人具有很好的参考价值(the code of FFT)

本程序为类人机器人的一个运动学正解程序，非常好的参考程序，可以继续在上面扩充(This program is a humanoid robot kinematics program, very good reference program, you can continue to expand in the above)

巴克码的编码程序，基础程序可以好好学习下，希望有帮助！(Barker coding procedures, based on the program can learn under, want to help!)

谱减法语音增强，包含基本谱减法，多带谱减法，以及自适应谱减法。(Spectral subtraction speech enhancement, contains the basic spectral subtraction, multi-band spectral subtraction, and adaptive spectral subtraction)

使用simulink对采集的数据进行处理所需要的设置。(Using simulink processing the collected data set required.)

对大量数据进行pca分析，得到表达信息的重要成分，压缩数据(PCA)

this matlab code compute COPT (a table for calculating risk in power system and reliability analysis.

这是模式识别中最小错误率Bayes分类器设计方案。 自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，通过比较概率大小判断一个体重身高二维向量代表的人是男是女。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小错误率贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到不同先验概率条件下错误率的统计。 (This is the minimum error rate pattern recognition Bayes classifier design. Self- improvement prior probability in different conditions , male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector third step is to application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Call the minimum error rate decision Functions Bayesian)