Multivariable-Feedback-Control

用小数据量方法求最大lyapunov指数程序设计(With a small amount of data method, to calculate the largest index of program design lyapunov)

可在MATLAB 5或MATLAB 6环境下运行，用于模式识别，人工智能领域。(In MATLAB 5 or MATLAB 6 environment running for pattern recognition, artificial intelligence.)

自适应波束形成算法，有需要的可以下载看看(Adaptive beamforming algorithm, there is a need can be downloaded)

描写了语音增强算法中的谱减法，从有噪声的语音信号中提取尽可能纯净的原始语音(Describe the spectral subtraction in speech enhancement algorithm, noisy speech signal as much as possible to extract the purity of the original speech )

单纯型算法和最速下降法的综合算法，能很快的搜索到最小值。(Simplex algorithm and steepest descent method of synthesis algorithms to quickly search the minimum.)

这是关于模拟调制的MATLAB源代码，希望对大家有所帮助(This is on the MATLAB simulation of modulation source code, I hope all of you to help)

All signal demo in MATLAB program

source code for video algorithm in matlab. a commercial video conferencing (cool).

元胞自动机的8个常用的matlab代码 里面有基础的一些 还有经典的。(Cellular automata eight commonly used matlab code inside have some of the classic and foundation. )

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，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。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，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，放入决策数组中。 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”，调用最小风险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，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability 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 The third step is the 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 . Bayesian classifier )