IncrementalProtocolSymetricNetwork

包括蚁群优化算法，梯度优化算法两个文档。(Including ant colony optimization algorithms, gradient optimization algorithm the two documents.)

kalman滤波，matlab实现————————————————(kalman filtering, matlab achieve----------------)

matlab下编写的三步快速搜索法，希望大家有用(Three Step Search Algorithm)

清晰易懂的MATLAB学习资料，描述了如何使用MATLAB解决各种实际问题(Clear and understandable information to learn MATLAB, describes how to use MATLAB to solve practical problems)

说明：  is a small example of how to use a slider and observe and enter the current value in the slider

非均匀天线组阵SUMPLE权值信噪比及信号合成性能分析(Non-uniform antenna arraying SUMPLE weights SNR performance analysis and signal synthesis)

循环平稳信号的空间谱估计课件，应该对大家有帮助(Spatial spectrum estimation cyclostationary signals)

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 )

《MATLAB R2008图形与动画编程实例教程》的源代码--第六章(" MATLAB R2008 examples of graphics and animation programming tutorial" source code- Chapter VI)

一种传统搜索优化算法 不需要对目标函数求导 得到很好的优化效果(A traditional search algorithm does not need to differentiate the objective function ,also gets well optimized results)