RGLS

matlab中的clock函数与进度条联合使用例子(matlab in clock function in combination with the progress bar examples)

This function quickly plots raster plots of large quantities of spike train data. Inputs can be spike times or binary spike train data organized by trial. It can also plot well-formatted single spike trains.

说明：  汉明码生成，74和1511汉明码的生产，以及其检错能力的验证(Hamming code generation, 74 and the 1511 production of Hamming codes and their error detection ability for)

说明：  基于模拟退火算法的TSP问题解算方法和二维路径规划(Based on simulated annealing algorithm for TSP solution method and two-dimensional path planning)

对一个三次函数寻找最优解（最小值），分别使用全局搜索法、二分法、黄金分割法和斐波那契法共四种方法寻优。函数可在程序中自行指定，有gui界面。(A cubic function to find the optimal solution (minimum), respectively, using a global search method, dichotomy, golden section and Fibonacci optimization method a total of four methods. Function can be specified in the program itself, there are gui interface.)

这是一个2fsk的跳频系统程序，很有帮助的，(This is a 2fsk hopping system procedures, helpful,)

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)

关于遗传算法简单入门的一些小程序，有选择、交叉、变异的函数(On the simple introduction of genetic algorithm some small programs, there is a choice, cross, mutation function)

用matlab开发语言实现的遗传算法例程，实现tsp问题的解答(Using matlab programming language implementation of genetic algorithm routines, implementation tsp questions)

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