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自动排序算法 基于matlab语言 实现对一数组利用自动排序算法 实现排序(Automatic sorting algorithm is based on an array matlab language to use automatic sorting algorithm sorting)

使用matlab通过微弯波导和直波导耦合设计一个3db耦合器(Through the use of matlab Microbend waveguide and the straight waveguide coupled design a 3db coupler)

此源码是关于TS模糊神经网络的matlab源代码，已经运行过。(This source is on TS fuzzy neural network matlab source code, has been run over.)

说明：  MNIST手势识别数据库进行softmax回归分类，直接运行softmaxExercise.m文件即可运行。(The MNIST gesture recognition database is classified by soft Max regression and can be run directly by running the softmaxExercise. m file.)

matlab遗传算法工具箱函数及实例讲解1 核心函数： 初始种群的生成函数 (matlab genetic algorithm toolbox and examples of function on one core function: the initial population of the generating function)

LMS算法实现自适应滤波 clear close all clc N=10000 设置仿真长度 信号产生参数设定 a1=-0.195 a1=-1.5955 a2=0.95 R0=[1,a1,a2 a1,1+a2,0 a2,a1,1] p=[1,0,0] r=inv(R0)*p 计算理论自相关函数 R=[r(1),r(2) r(2),r(1)] 生成理论自相关矩阵 p1=[r(2),r(3)] 生成互相关 h=inv(R)*p1 计算维纳解 Jmin=r(1)-h *p1 计算维纳解时最小均方误差 u=1/sum(eigs(R)) ( LMS算法实现自适应滤波 clear close all clc N=10000 设置仿真长度 信号产生参数设定 a1=-0.195 a1=-1.5955 a2=0.95 R0=[1,a1,a2 a1,1+a2,0 a2,a1,1] p=[1,0,0] r=inv(R0)*p 计算理论自相关函数 R=[r(1),r(2) r(2),r(1)] 生成理论自相关矩阵 p1=[r(2),r(3)] 生成互相关 h=inv(R)*p1 计算维纳解 Jmin=r(1)-h*p1 计算维纳解时最小均方误差 u=1/sum(eigs(R)) )

产生 正态白噪声序列 (1) 打印出前50个数 (2) 分布检验 (3) 均值检验 (4) 方差检验 (5) 计算相关函数 Bx(i),i=0,±1,±2,…, ±10。 B(m)=1/1000 (Have a normal white noise sequence (1) print out the top 50 the number of (2) the distribution of test (3) the mean test (4) of variance test (5) calculating the correlation function Bx (i), i = 0, ± 1, ± 2 , ..., ± 10. B (m) = 1/1000)

基于ukf目标追踪，包含sigma采样、ut变换、ukf的m文件和一个简单的追踪实例(Ukf-based target tracking, including sigma sampling, ut transform, ukf m-files and a simple trace instances)

说明：  《变结构控制与MATLAB仿真》书中第二章例六的变结构控制程序( Variable Structure Control and MATLAB Simulation book chapter six cases of variable structure control procedures)

训练集/测试集产生 load spectra_data.mat 随机产生训练集和测试集 temp = randperm(size(NIR,1)) 训练集——50个样本 P_train = NIR(temp(1:50),:) T_train = octane(temp(1:50),:) 测试集——10个样本 P_test = NIR(temp(51:end),:) T_test = octane(temp(51:end),:) N = size(P_test,2) 数据归一化 ( Training set/test set generation load spectra_data.mat randomly generated training set and test set temp = randperm (size (NIR, 1)) training set- 50 samples P_train = NIR (temp (1:50) ,:)' T_train = octane (temp (1:50 ),:)' test set- 10 samples P_test = NIR (temp (51: end ),:)' T_test = octane (temp (51 : end ),:)' N = size (P_test, 2) Data Normalization)