Adaline1

Gaussian Distribution.

this is the matlab code for dip lab programs

matlab求解时滞微分方程教程，很详细，有例子。(Solving delay differential equations matlab tutorial)

下垂控制，两个微电源，孤岛运行和并网运行(droop control，two DG )

通过对输入ISBN进行演算.从而得出其真伪信息(Input to calculations by ISBN. To arrive at the authenticity of the information)

异步电动机数量控制完整仿真模型，可以根据实际情况写该参数，达到研究者所需要的情形(The number of complete induction motor control simulation model, the parameter can be written according to the actual situation, the researchers needed to achieve the situation)

matlab讲义！各种基本知识点和使用方法，源程序用法！(matlab handouts! Basic knowledge and use, source usage!)

Corner_Matching_by_Invariant_Moments.rar INDEX: 1- InvariantMomentsofCorners.m Calculate moment invariants on the selected window of the corner. 2- invariantmoments.m Calculates Moment invariants

DFT：离散傅立叶变换 符运行后得频谱图(DFT: Discrete Fourier Transform running at a frequency spectrum)

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 )