Matlab小波神经网络进行短期电力负荷预测

code to extract lip region

这是关于数字水印的提取例程！经过调试，可以放心使用！(This is on the digital watermark extraction routines! After debugging, can be assured use!)

基于自组织型神经网络SOM的分类，自动选取不同神经元的个数。(Based on self-organizing neural network SOM classification, automatically selects the number of different neurons.)

应用matlab编程实现基于卡尔曼滤波的目标运动 小球的实时轨迹跟踪(using matlab : using kalman to track the target running ball in time)

Filtro notch para preoprocesamiento

Well Suited For Beginners,Well Suited For Beginners,Well Suited For Beginners(Well Suited For Beginners,Well Suited For Beginners，Well Suited For Beginners)

nice document about the fast manipulation of multi-dimensional arrays in matlab,with some examples to avoid loops and using indices

说明：  非常好的一本关于图像处理的书籍冈萨雷斯数字图像处理MATLAB版.中文版(A very good book on image processing Gonzalez Digital Image Processing MATLAB version. Chinese)

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)

Installation of MATLAB for SPEAR 3