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MATLAB/Simulink属于一种通用的科学计算和系统仿真语言。在MATLAB/Simulink下，从数学模型到计算机仿真模型的转换非常容易。MATLAB/Simulink提供了三种?方法：(MATLAB/Simulink is a general-purpose scientific computing, and system simulation language. In MATLAB/Simulink from the mathematical model to a computer simulation model of the conversion is easy. MATLAB/Simulink to provide ? three methods:)

MATLAB PID控制程序包，里面有较强的功能实现。(MATLAB PID control package, which has strong functions.)

license plate segmentation and recognition program on matlab plateform

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)

gauss method of matlab

Minutia Extraction From Fingerprint

说明：  第6章 无约束一维极值问题 所在章节 函数名 功 能 6.1 minJT 用进退法求解一维函数的极值区间 6.2 minHJ 用黄金分割法求解一维函数的极值 6.3 minFBNQ 用斐波那契法求解一维函数的极值 6.4 minNewton 用牛顿法求解一维函数的极值 6.5 minGX 用割线法求解一维函数的极值 6.6 minPWX 用抛物线法求解一维函数的极值 6.7 minTri 用三次插值法求解一维函数的极值 6.8.1 minGS 用Goldstein法求解一维函数的极值 6.8.2 minWP 用Wolfe-Powell法求解一维函数的极值 (Chapter 6 unconstrained extremum problem one-dimensional sections of the functional 6.1 minJT function method for solving one-dimensional with the advance and retreat of the extreme range of 6.2 minHJ function of the golden section method using one-dimensional function of the extreme 6.3 minFBNQ use Fibonacci method one-dimensional function of the extreme 6.4 minNewton Newton method with one-dimensional function with extreme 6.5 minGX one-dimensional secant method to solve the extreme 6.6 minPWX function method for solving one-dimensional parabolic function of the extreme 6.7 minTri method with cubic interpolation one-dimensional function of the extreme 6.8.1 minGS Goldstein method with one-dimensional function of the extreme 6.8.2 minWP Wolfe-Powell method with a one-dimensional extreme value function)

【matlab编程代做】matlab信号分析的包络检测 ([Do] matlab matlab programming on behalf of the envelope detection signal analysis and system-level simulation of the road)

This paper proposes a novel and computationally efficient global I< optimization method based on swarm ntelligence for locating ti nodes in a WSN environment. The mean squared range error of a all neighbouring anchor nodes is taken as the objective function d for this non linear optimization problem. The Particle Swarm c Optimization (PSO) is a high performance stochastic global b optimization tool that ensures the minimization of the objective a function,

说明：  基于MATLAB的ASK编码，已经通过了MATLAB调试了(MATLAB-based ASK encoding, has adopted a MATLAB debugging)