polardb

使用bp预测模型，通过matlab变成实现对现实生活进行应用于预测。(the real example for the bp model to forecast something)

Fisherface方法的实现是在PCA数据重构的基础上完成的，首先利用PCA将高维数据投影到低维特征脸子空间，然后再在这个低维特征脸子空间上用LDA特征提取方法得到Fisherface。程序中使用参数寻优的方法来寻找最佳投影维数，以达到比较理想的识别效果。 (The Fisherface method implemented in the PCA data reconstruction based on the completion of the first use of PCA projection of high-dimensional data to a low dimensional feature subspace, and then on the characteristics of low-dimensional subspace LDA feature extraction methods to get the Fisherface. Program parameter optimization method is used to find the best projection dimension, in order to achieve the ideal identification.)

共生矩阵的实现及特征提取方法，里面包含了各个特征向量提取的子程序，可以在其他程序中直接调用。(co-occurrence matrix and the realization of feature extraction methods, each of which contains a feature vector extraction subroutine, the other procedures can be called directly.)

采用db3小波对信号进行六层分解，可以用作容错故障检测的源程序，我做的小论文就用的这个程序，没有错误(Using six db3 wavelet decomposition of the signal can be used as fault tolerance fault detection of the source, I made a small paper on the use of this program, no errors)

说明：  Switched Reluctance Motor

联合智能(JI-MUD)多用户检测算法是由粒子群优化(PSO)算法,遗传算法(GA)和模拟退火(SA)算法(the joint intelligent multi-user detection (JI-MUD) algorithm which was composed by particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and simulated annealing (SA) algorithm was proposed)

利用潮位数据计算理论深度基准面，其中各分潮系数需修改(Calculating theoretical depth datum using tidal data)

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)

这个文章是关于基本matlab函数优化的讲解，新手和老手都会从中得益。(This article is about the basic matlab function optimization explanations, novices and old hands will benefit from it.)

该代码是书籍源码，是matlab的书的源代码，里面是算法实例，(The source code is the book, the book is the matlab source code, which is the algorithm instance,)