rsum02

英文资料。关于机器人视觉伺服的全面综述。有参考价值。(English. Servo robot vision of the comprehensive overview. Reference value.)

the last part of my work in the photovoltaic generator

这是一篇介绍matlab函数使用手册的文章，通过它可以快速查找函数的功能等。(This is a matlab function to introduce the use of manual article, which can quickly find the function function.)

）网络是一种重要的神经网络，RBF网络的训练分为两步，第一步是通过聚类算法得到初始的权值，第二步是根据训练数据训练网络的权值。() Network is an important neural network, RBF network training is divided into two steps, the first step is the initial weights clustering algorithm, the second step is based on the training data to train the network weights.)

说明：  这是一偏讲述有关移动机器人路径规划的文章，很好的(This is a one-sided article tells about mobile robot path planning, good)

用rbf网络 实现神经网络学习算法 可以在matlab直接运行 其中有些参数可以自由根据情况改动(rbf )

在MATLAB环境下使用谱减法对语音信号进行增强，程序可以运行(In the MATLAB environment using spectral subtraction speech signal enhancement program can run)

说明：  This function solves the mixed integer linear programming problems. It uses the linprog.m function that comes with the optimization toolbox of MATLAB. It employs the branch and bound algorithm. It uses depth first search.

蒙特卡罗方法在随机上的应用和软件实现方法，里面以课件形式教你如果使用蒙特卡罗(mente carlo)

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 )