├── MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载 ├── 第10章 │ ├── 2.bmp │ ├── im2.bmp │ ├── ysw10_1.m │ └── ysw10_2.m ├── 第1章 │ ├── EM_pdf_est.m │ ├── Gaussian_ML_estimate.m │ ├── Parzen_gauss_kernel.m │ ├── bayes_classifier.m │ ├── comp_gauss_dens_val.m │ ├── compute_error.m │ ├── em_alg_function.m │ ├── euclidean_classifier.m │ ├── gauss.m │ ├── generate_gauss_classes.m │ ├── k_nn_classifier.m │ ├── knn_density_estimate.m │ ├── mahalanobis_classifier.m │ ├── mixt_model.m │ ├── mixt_value.m │ ├── mixture_Bayes.m │ ├── plot_data.m │ ├── ysw1_1.m │ ├── ysw1_10.m │ ├── ysw1_11.m │ ├── ysw1_12.m │ ├── ysw1_13.m │ ├── ysw1_2.m │ ├── ysw1_3.m │ ├── ysw1_4.m │ ├── ysw1_5.m │ ├── ysw1_6.m │ ├── ysw1_7.m │ ├── ysw1_8.m │ └── ysw1_9.m ├── 第2章 │ ├── 1.jpg │ ├── 139.jpg │ ├── 140.jpg │ ├── 145.jpg │ ├── 19.jpg │ ├── 20.jpg │ ├── 30.jpg │ ├── 60.jpg │ ├── 79.jpg │ ├── 80.jpg │ ├── ysw2_1.m │ ├── ysw2_2.m │ └── ysw2_3.m ├── 第3章 │ └── ysw3_1.m ├── 第4章 │ ├── ysw4_1.m │ ├── ysw4_2.m │ └── 检验样本图片 │ │ ├── num0_1.bmp │ │ ├── num1_1.bmp │ │ ├── num2_1.bmp │ │ ├── num3_1.bmp │ │ ├── num4_1.bmp │ │ └── num5_1.bmp ├── 第5章 │ ├── ysw5_1.m │ └── ysw5_2.m ├── 第6章 │ ├── GA_BP_1 │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Decode.m │ │ ├── GA_BP_sep.m │ │ ├── MRI_pic.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── data.mat │ │ ├── fun.m │ │ ├── test.m │ │ ├── ysw.fig │ │ └── ysw.m │ └── GA_BP_2 │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Decode.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── data1.mat │ │ ├── data1_GA_BP.mat │ │ ├── data1_GA_BP_test_result.mat │ │ ├── data2.mat │ │ ├── data2_GA_BP.mat │ │ ├── data2_GA_BP_test_result.mat │ │ ├── error_hg.mat │ │ ├── error_hg_2.mat │ │ ├── fun.m │ │ ├── kth.m │ │ ├── kth_2.m │ │ ├── surf3D.m │ │ ├── surf3D_2.m │ │ ├── surf3D_GA_BP.m │ │ ├── surf3D_GA_BP_2.m │ │ ├── test.m │ │ ├── ysw1.m │ │ └── ysw2.m ├── 第7章 │ ├── 3.jpg │ ├── LOD78.csv │ ├── Untitled.m │ ├── dbox.m │ ├── engine.wav │ ├── myfractal.m │ ├── ysw7_1.m │ ├── ysw7_1_1.m │ └── ysw7_2.m ├── 第8章 │ ├── CO2.mat │ ├── CO2chazhi.m │ ├── LINGO2.lg4 │ ├── mstdata1.mat │ ├── mstyuce1.m │ ├── y3.m │ └── yc.mat └── 第9章 │ ├── 1.jpg │ ├── b.jpg │ ├── canny.m │ ├── morphological.m │ ├── prewitt.m │ ├── sobel.m │ ├── ysw9_1.m │ ├── ysw9_2.m │ └── ysw9_3.m ├── MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载 ├── 第11章 │ ├── Pc1.mat │ ├── Pc2.mat │ ├── Pc3.mat │ ├── Pc4.mat │ ├── Pc5.mat │ ├── Pc6.mat │ ├── Pg1.mat │ ├── Pg2.mat │ ├── Pg3.mat │ ├── Pg4.mat │ ├── Pg5.mat │ ├── Pg6.mat │ ├── Q.mat │ ├── data1.mat │ ├── data2.mat │ ├── error_hg.mat │ ├── error_hg_2.mat │ ├── v1.mat │ ├── v2.mat │ ├── v3.mat │ ├── v4.mat │ ├── v5.mat │ ├── v6.mat │ ├── ysw1.m │ ├── ysw3.m │ ├── ysw6.m │ └── ysw8.m ├── 第12章 │ ├── 关联度计算 │ │ ├── data.mat │ │ ├── plot_ysw1.m │ │ ├── ysw1.m │ │ ├── ysw2.m │ │ ├── ysw3.m │ │ ├── ysw4.m │ │ ├── ysw6.m │ │ ├── ysw7.m │ │ ├── ysw8.m │ │ └── 数据.xls │ └── 非线性回归 │ │ ├── myfun.m │ │ ├── myfun1.m │ │ ├── ysw1.m │ │ ├── ysw1_1.m │ │ ├── ysw2.m │ │ ├── ysw3.m │ │ ├── ysw38.m │ │ ├── ysw4.m │ │ ├── ysw6.m │ │ ├── ysw7.m │ │ ├── ysw8.m │ │ └── 数据.xls ├── 第13章 │ └── 布朗运动 │ │ ├── geometric_brownian.m │ │ ├── simbrownian.m │ │ ├── ysw1.m │ │ ├── ysw2.m │ │ └── ysw3.m ├── 第14章 │ ├── P.mat │ ├── RandomPermutation.m │ ├── Rf_Q_learning.m │ ├── conx.mat │ ├── dijf.m │ ├── dijkstra.m │ ├── dijz.m │ ├── node_xy_int.mat │ ├── person.m │ ├── prim.m │ ├── sort_mat.m │ ├── ysw1.m │ ├── yx1.m │ └── yx2.m ├── 第15章 │ ├── Cross.m │ ├── Mutation.m │ ├── Select.m │ ├── createfigure.m │ ├── fun.m │ ├── pop_meet_conditions.m │ ├── test.m │ ├── ysw15_1.m │ ├── ysw2.m │ ├── 公交系统模拟 │ │ └── ysw15_2.m │ └── 说明.m ├── 第16章 │ ├── 1.jpg │ ├── rgb2hsv.m │ ├── rgb2ycrcb.m │ ├── waitopen.m │ ├── xbb.jpg │ ├── xbb2.jpg │ ├── ysw16_1.m │ ├── ysw16_2.m │ ├── ysw16_3.m │ ├── ysw16_4.m │ ├── ysw16_5.m │ ├── ysw16_6.m │ └── ysw16_7.m ├── 第17章 │ └── 人足迹特征比对系统设计 │ │ ├── Euc_dist.mat │ │ ├── FileFullName.mat │ │ ├── RGB_panel.m │ │ ├── bw_pic │ │ ├── 1.bmp │ │ ├── 10.bmp │ │ ├── 11.bmp │ │ ├── 12.bmp │ │ ├── 13.bmp │ │ ├── 14.bmp │ │ ├── 15.bmp │ │ ├── 16.bmp │ │ ├── 17.bmp │ │ ├── 18.bmp │ │ ├── 19.bmp │ │ ├── 2.bmp │ │ ├── 20.bmp │ │ ├── 21.bmp │ │ ├── 22.bmp │ │ ├── 23.bmp │ │ ├── 24.bmp │ │ ├── 25.bmp │ │ ├── 26.bmp │ │ ├── 27.bmp │ │ ├── 28.bmp │ │ ├── 29.bmp │ │ ├── 3.bmp │ │ ├── 30.bmp │ │ ├── 31.bmp │ │ ├── 4.bmp │ │ ├── 5.bmp │ │ ├── 6.bmp │ │ ├── 7.bmp │ │ ├── 8.bmp │ │ └── 9.bmp │ │ ├── bw_pic_corr │ │ ├── 1.bmp │ │ ├── 10.bmp │ │ ├── 11.bmp │ │ ├── 12.bmp │ │ ├── 13.bmp │ │ ├── 14.bmp │ │ ├── 15.bmp │ │ ├── 16.bmp │ │ ├── 17.bmp │ │ ├── 18.bmp │ │ ├── 19.bmp │ │ ├── 2.bmp │ │ ├── 20.bmp │ │ ├── 21.bmp │ │ ├── 22.bmp │ │ ├── 23.bmp │ │ ├── 24.bmp │ │ ├── 25.bmp │ │ ├── 26.bmp │ │ ├── 27.bmp │ │ ├── 28.bmp │ │ ├── 29.bmp │ │ ├── 3.bmp │ │ ├── 30.bmp │ │ ├── 31.bmp │ │ ├── 4.bmp │ │ ├── 5.bmp │ │ ├── 6.bmp │ │ ├── 7.bmp │ │ ├── 8.bmp │ │ └── 9.bmp │ │ ├── canny_seg.m │ │ ├── colorsPlot.m │ │ ├── correct_pic │ │ ├── 1.bmp │ │ ├── 10.bmp │ │ ├── 11.bmp │ │ ├── 12.bmp │ │ ├── 13.bmp │ │ ├── 14.bmp │ │ ├── 15.bmp │ │ ├── 16.bmp │ │ ├── 17.bmp │ │ ├── 18.bmp │ │ ├── 19.bmp │ │ ├── 2.bmp │ │ ├── 20.bmp │ │ ├── 21.bmp │ │ ├── 22.bmp │ │ ├── 23.bmp │ │ ├── 24.bmp │ │ ├── 25.bmp │ │ ├── 26.bmp │ │ ├── 27.bmp │ │ ├── 28.bmp │ │ ├── 29.bmp │ │ ├── 3.bmp │ │ ├── 30.bmp │ │ ├── 31.bmp │ │ ├── 4.bmp │ │ ├── 5.bmp │ │ ├── 6.bmp │ │ ├── 7.bmp │ │ ├── 8.bmp │ │ └── 9.bmp │ │ ├── database.mat │ │ ├── duosuanzironghe.m │ │ ├── erjieweifen.m │ │ ├── footprint.fig │ │ ├── footprint.m │ │ ├── gaborfilter.m │ │ ├── gaborfilter_pic.m │ │ ├── kirsch_algorithm.m │ │ ├── kirsch_seg.m │ │ ├── laplacian_seg.m │ │ ├── prewitt_seg.m │ │ ├── rgb2hsi.m │ │ ├── robetrs_seg.m │ │ ├── sobel_seg.m │ │ ├── szh.m │ │ ├── ysw1.m │ │ ├── ysw2.m │ │ ├── zft.m │ │ └── zhongz.m ├── 第18章 │ └── ysw1.m ├── 第19章 │ ├── Y2.jpg │ ├── adj_node.mat │ ├── adj_node_2.mat │ ├── city_car_sim.m │ ├── dijkstra.m │ ├── doubleAP.m │ ├── node_AP.mat │ ├── node_car_2.mat │ ├── prim.m │ ├── shizilukou1.m │ ├── shizilukou2.m │ ├── singleAP.m │ ├── xianlusim1.m │ ├── xianlusim2.m │ ├── y1.m │ ├── y2.m │ ├── y3.jpg │ └── y4.jpg └── 第20章 │ └── immunity │ ├── best.m │ ├── calfitvalue.m │ ├── calobjvalue.m │ ├── calx.m │ ├── crossover.m │ ├── decodebinary.m │ ├── decodechrom.m │ ├── hjjsort.m │ ├── initpop.m │ ├── main.m │ ├── mutation.m │ ├── resultselect.m │ ├── selection.m │ └── ysw_x.mat ├── MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载 ├── 第21章 │ ├── fitness.m │ └── ysw21_1.m ├── 第22章 │ ├── ysw1ctrl.m │ ├── ysw1plant.m │ ├── ysw1plot.m │ ├── ysw1sim.mdl │ ├── ysw1sim.mdl.r2011a │ ├── ysw2ctrl.m │ ├── ysw2ctrl_sine.m │ ├── ysw2plant.m │ ├── ysw2plant_sine.m │ ├── ysw2plot.m │ ├── ysw2plot_sine.m │ ├── ysw2sim.mdl │ ├── ysw2sim.mdl.r2011a │ ├── ysw2sim_sine.mdl │ ├── ysw_PID.m │ ├── ysw_PID1.slx │ └── ysw_PID2.mdl ├── 第23章 │ ├── Fitness_ACO.m │ └── main_ACO.m ├── 第24章 │ ├── GSA.m │ ├── Gconstant.m │ ├── Gfield.m │ ├── evaluateF.m │ ├── initialization.m │ ├── main.m │ ├── massCalculation.m │ ├── move.m │ ├── space_bound.m │ ├── test_functions.m │ └── test_functions_range.m ├── 第25章 │ ├── fitness.m │ └── ysw1.m ├── 第26章 │ ├── Hungarian.m │ ├── Hungarian_algorithm.m │ └── ysw1.m ├── 第27章 │ ├── Sphere.m │ ├── ackley.m │ ├── calculateFitness.m │ ├── griewank.m │ ├── rastrigin.m │ ├── rosenbrock.m │ ├── schwefel.m │ └── ysw1.m ├── 第28章 │ ├── GA │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── data.mat │ │ ├── fun.m │ │ ├── test.m │ │ └── ysw1.m │ ├── IGA │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── data.mat │ │ ├── fun.m │ │ ├── test.m │ │ └── ysw2.m │ └── 合并 │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Cross1.m │ │ ├── Mutation.m │ │ ├── Mutation1.m │ │ ├── Select.m │ │ ├── Select1.m │ │ ├── data.mat │ │ ├── fun.m │ │ ├── fun1.m │ │ ├── test.m │ │ ├── ysw1.m │ │ ├── ysw2.m │ │ └── ysw3.m ├── 第29章 │ ├── fitness.m │ └── ysw1.m └── 第30章 │ ├── AF_dist.m │ ├── AF_follow.m │ ├── AF_foodconsistence.m │ ├── AF_init.m │ ├── AF_prey.m │ ├── AF_swarm.m │ ├── dist.m │ ├── myfun.m │ ├── ysw1.m │ └── ysw2.m ├── MATLAB优化算法案例分析与应用（进阶篇）教学PPT 1-15章 ├── 第10章 指纹图像细节特征提取与MATLAB实现.ppt ├── 第11章 基于多元回归模型的矿井通风量计算.ppt ├── 第12章 基于非线性多混合函数拟合模型的植被过滤带计算.ppt ├── 第13章 基于伊藤微分方程的布朗运动分析.ppt ├── 第14章 基于Q学习的无线体域网路由方法.ppt ├── 第15章 基于遗传算法的公交排班系统分析.ppt ├── 第1章 基于贝叶斯分类器的数据处理与MATLAB实现.ppt ├── 第2章 基于背景差分的运动目标检测与MATLAB实现.ppt ├── 第3章 基于小波变换的图像压缩与MATLAB实现.ppt ├── 第4章 基于BP的模型优化预测与MATLAB实现.ppt ├── 第5章 基于RLS算法的数据预测与MATLAB实现.ppt ├── 第6章 基于GA优化的BP网络算法分析与MATLAB实现.ppt ├── 第7章 分形维数应用与MATLAB实现.ppt ├── 第8章 碳排放约束下的煤炭消费量优化预测.ppt └── 第9章 焊缝边缘检测算法对比分析与MATLAB实现.ppt ├── MATLAB优化算法案例分析与应用（进阶篇）教学PPT 16-30章 ├── 第16章 人脸检测与MATLAB实现.ppt ├── 第17章 基于改进的多算子融合的图像识别系统设计.ppt ├── 第18章 基于罚函数的粒子群算法的函数寻优.ppt ├── 第19章 车载自组织网络中路边性能及防碰撞算法研究.ppt ├── 第20章 基于免疫算法的数值逼近优化分析.ppt ├── 第21章 基于启发式算法的函数优化分析.ppt ├── 第22章 一级倒立摆变结构控制系统设计与仿真研究.ppt ├── 第23章 基于蚁群算法的函数优化分析.ppt ├── 第24章 基于引力搜索算法的函数优化分析.ppt ├── 第25章 基于细菌觅食算法的函数优化分析.ppt ├── 第26章 基于匈牙利算法的指派问题优化分析.ppt ├── 第27章 基于人工蜂群算法的函数优化分析.ppt ├── 第28章 基于改进的遗传算法的城市交通信号优化.ppt ├── 第29章 基于差分进化算法的函数优化分析.ppt └── 第30章 基于鱼群算法的函数优化分析.ppt └── README.md /MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载/第10章/2.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codes-books/matlab-optimization-advanced/31bd3a38557ddd17e366849bdd83262020e7ae42/MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载/第10章/2.bmp -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载/第10章/im2.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codes-books/matlab-optimization-advanced/31bd3a38557ddd17e366849bdd83262020e7ae42/MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载/第10章/im2.bmp -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载/第10章/ysw10_1.m: -------------------------------------------------------------------------------- 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classes. It is assumed that the pdf of 7 | % each class is a mixture of Gaussians, that the respective covariance 8 | % matrices are diagonal and that the covariance matrix of the i-th 9 | % distribution has the form sigma_i^2 I. 10 | % 11 | % INPUT ARGUMENTS: 12 | % X: lxN matrix whose columns are the data vectors. 13 | % y: N-dimensional vector contanining the class labels for each 14 | % one of the data vectors of X. 15 | % m_ini: cl-dimensional object array, whose i-th element is an lxci 16 | % array. The j-th column of the later corresponds to the initial 17 | % estimate of the mean of the j-th normal distribution involved 18 | % in the mixture for the pdf of the i-th class. 19 | % s_ini: cl-dimensional object array, whose i-th element is a ci 20 | % dimensional array whose i-th component is the variance for the 21 | % i-th class. 22 | % P_ini: cl-dimensional object array, whose i-th element is a ci 23 | % dimensional array containing the initial estimate of the mixing 24 | % probabilities of the normal distributions for the pdf of the 25 | % i-th class. 26 | % 27 | % OUTPUT ARGUMENTS: 28 | % m: it has the same structure with m_ini and contains the final 29 | % estimates of the means of the normal distributions. 30 | % s: it has the same structure with s_ini and contains the final 31 | % estimates of the variances of the normal distributions. 32 | % P: it has the same structure with P_ini and contains the final 33 | % estimates of the mixing probabilities of the normal 34 | % distributions. 35 | % P_cl: a cl-dimensional array whose i-th component is the estimated a 36 | % priori probability of the i-th class. It is estimated as the 37 | % ratio of the number of vectors of the i-th class to the total 38 | % number of the data vectors. 39 | % 40 | % (c) 2010 S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras 41 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 42 | 43 | [l,N]=size(X); 44 | e_min=10^(-5); %This parameter is used in the termination criterion 45 | %of the EM algorithm 46 | 47 | % Separate the vectors of different classes and estimate of the a 48 | % priori class probabilities 49 | cl=max(y); % Total number of classes 50 | acc_tot=[]; % Count the number of points per class 51 | for j=1:cl 52 | temp=[]; 53 | t=0; 54 | for i=1:N 55 | if(y(i)==j) 56 | temp=[temp X(:,i)]; 57 | t=t+1; 58 | end 59 | end 60 | acc_tot=[acc_tot t]; 61 | Xs{j}=temp; 62 | end 63 | P_cl=acc_tot/N; 64 | 65 | for j=1:cl 66 | [m{j},s{j},P{j},iter1,Q_tot,e_tot]=em_alg_function(Xs{j},m_ini{j},s_ini{j},P_ini{j},e_min); 67 | m{j}=m{j}'; 68 | end 69 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载/第1章/Gaussian_ML_estimate.m: -------------------------------------------------------------------------------- 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To generate X, utilize the function mixt_model 4 | randn('seed',0); 5 | m1=[1, 2]'; m2=[2, 3]'; 6 | m=[m1 m2]; 7 | S(:,:,1)=[0.5 -0.05; -0.05 0.1]; 8 | S(:,:,2)=[0.2 0; 0 0.2]; 9 | P=[1/2 1/2]; 10 | N=1000; 11 | sed=0; 12 | [X,y]=mixt_model(m,S,P,N,sed); 13 | figure(1); 14 | plot(X(1,:),X(2,:),'.'); 15 | 16 | %% 2. Repeat step 1 for P1 = 0.85, P2 = 0.15. 17 | clc,clear,close all 18 | warning off 19 | randn('seed',0); 20 | m1=[1, 2]'; m2=[2, 3]'; 21 | m=[m1 m2]; 22 | S(:,:,1)=[0.5 -0.05; -0.05 0.1]; 23 | S(:,:,2)=[0.2 0; 0 0.2]; 24 | P=[0.75 0.25]; 25 | N=1000; 26 | sed=0; 27 | [X,y]=mixt_model(m,S,P,N,sed); 28 | figure(2); 29 | plot(X(1,:),X(2,:),'.'); 30 | 31 | %% 3. Play by changing the parameters of the covariance matrices and the mixing probabilities P1 and P2. 32 | clc,clear,close all 33 | warning off 34 | randn('seed',0); 35 | m1=[1, 2]'; m2=[2, 3]'; 36 | m=[m1 m2]; 37 | S(:,:,1)=[0.5 0.05; 0.05 0.1]; 38 | S(:,:,2)=[0.2 0; 0 0.2]; 39 | P=[0.5 0.5]; 40 | N=1000; 41 | sed=0; 42 | [X,y]=mixt_model(m,S,P,N,sed); 43 | figure(3); 44 | plot(X(1,:),X(2,:),'.'); 45 | 46 | randn('seed',0); 47 | m1=[1, 2]'; m2=[2, 3]'; 48 | m=[m1 m2]; 49 | S(:,:,1)=[0.5 0.05; 0.05 0.1]; 50 | S(:,:,2)=[0.2 0; 0 0.2]; 51 | P=[0.25 0.75]; 52 | N=1000; 53 | sed=0; 54 | [X,y]=mixt_model(m,S,P,N,sed); 55 | figure(4); 56 | plot(X(1,:),X(2,:),'.'); 57 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载/第1章/ysw1_8.m: -------------------------------------------------------------------------------- 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(im1+im2+im3+im4)/4; 8 | figure,imshow(im5,[]) 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载/第2章/ysw2_2.m: -------------------------------------------------------------------------------- 1 | clc,clear,close all 2 | warning off 3 | im1 = imread('19.jpg'); % im1 = im2double(im1); 4 | im2 = imread('20.jpg'); % im2 = im2double(im2); 5 | im3 = imread('79.jpg'); % im3 = im2double(im3); 6 | im4 = imread('80.jpg'); % im4 = im2double(im4); 7 | im5 = imread('139.jpg');% im5 = im2double(im5); 8 | im6 = imread('140.jpg');% im6 = im2double(im6); 9 | T = 20; 10 | im12 = im2-im1; im12 = im12(:,:,1) > T; 11 | figure,imshow(im12,[]) 12 | im34 = im4-im3; im34 = im34(:,:,1) > T; 13 | figure,imshow(im34,[]) 14 | im56 = im6-im5; im56 = im56(:,:,1) > 20; 15 | figure,imshow(im56,[]) 16 | 17 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）1-10章程序下载/第2章/ysw2_3.m: -------------------------------------------------------------------------------- 1 | clc,clear,close all 2 | warning off 3 | im1 = imread('1.jpg'); im1 = im2double(im1); 4 | im2 = imread('30.jpg'); im2 = im2double(im2); 5 | im3 = imread('60.jpg'); im3 = im2double(im3); 6 | im4 = imread('80.jpg'); im4 = im2double(im4); 7 | im6 = imread('145.jpg'); % im6 = im2double(im6); 8 | T = 20; 9 | im5 = (im1+im2+im3+im4)/4; im5 = im2uint8(im5); 10 | im1 = im2uint8(im1); im2 = im2uint8(im2); 11 | im3 = im2uint8(im3); im4 = im2uint8(im4); 12 | im15 = im1-im5; im15 = im15(:,:,1) > T; 13 | figure,imshow(im15,[]) 14 | im25 = im2-im5; im25 = im25(:,:,1) > T; 15 | figure,imshow(im25,[]) 16 | im35 = im3-im5; im35 = im35(:,:,1) > T; 17 | figure,imshow(im35,[]) 18 | im45 = im4-im5; im45 = im45(:,:,1) > T; 19 | figure,imshow(im45,[]) 20 | im65 = im6-im5; im65 = im65(:,:,1) > T; 21 | figure,imshow(im65,[]) 22 | -------------------------------------------------------------------------------- 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Author : abhiruplahiri@yahoo.com 4 | 5 | function [w] = simbrownian(t,n) 6 | randn('state',100); % setting state for repeated simulations give same result 7 | dt=t/n; 8 | w=zeros(1,n); 9 | dw=zeros(1,n); 10 | w(1)=0; 11 | for j=2:n 12 | w(j)=w(j-1)+sqrt(dt)*randn; 13 | end 14 | % plot([dt:dt:t],w,'r-') 15 | % grid 16 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载/第13章/布朗运动/ysw1.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codes-books/matlab-optimization-advanced/31bd3a38557ddd17e366849bdd83262020e7ae42/MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载/第13章/布朗运动/ysw1.m -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载/第13章/布朗运动/ysw2.m: -------------------------------------------------------------------------------- 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0.1587;0.1746 0.1746 0.1746;0.1905 0.1905 0.1905;0.2063 0.2063 0.2063;0.2222 0.2222 0.2222;0.2381 0.2381 0.2381;0.254 0.254 0.254;0.2698 0.2698 0.2698;0.2857 0.2857 0.2857;0.3016 0.3016 0.3016;0.3175 0.3175 0.3175;0.3333 0.3333 0.3333;0.3492 0.3492 0.3492;0.3651 0.3651 0.3651;0.381 0.381 0.381;0.3968 0.3968 0.3968;0.4127 0.4127 0.4127;0.4286 0.4286 0.4286;0.4444 0.4444 0.4444;0.4603 0.4603 0.4603;0.4762 0.4762 0.4762;0.4921 0.4921 0.4921;0.5079 0.5079 0.5079;0.5238 0.5238 0.5238;0.5397 0.5397 0.5397;0.5556 0.5556 0.5556;0.5714 0.5714 0.5714;0.5873 0.5873 0.5873;0.6032 0.6032 0.6032;0.619 0.619 0.619;0.6349 0.6349 0.6349;0.6508 0.6508 0.6508;0.6667 0.6667 0.6667;0.6825 0.6825 0.6825;0.6984 0.6984 0.6984;0.7143 0.7143 0.7143;0.7302 0.7302 0.7302;0.746 0.746 0.746;0.7619 0.7619 0.7619;0.7778 0.7778 0.7778;0.7937 0.7937 0.7937;0.8095 0.8095 0.8095;0.8254 0.8254 0.8254;0.8413 0.8413 0.8413;0.8571 0.8571 0.8571;0.873 0.873 0.873;0.8889 0.8889 0.8889;0.9048 0.9048 0.9048;0.9206 0.9206 0.9206;0.9365 0.9365 0.9365;0.9524 0.9524 0.9524;0.9683 0.9683 0.9683;0.9841 0.9841 0.9841;1 1 1]); 15 | 16 | % Create subplot 17 | subplot1 = subplot(2,2,1,'Parent',figure1,'YDir','reverse','Layer','top'); 18 | % Uncomment the following line to preserve the X-limits of the axes 19 | xlim([0.5 320.5]); 20 | % Uncomment the following line to preserve the Y-limits of the axes 21 | ylim([0.5 240.5]); 22 | box('on'); 23 | hold('all'); 24 | 25 | % Create image 26 | image(red,'Parent',subplot1,'CDataMapping','scaled'); 27 | 28 | % Create title 29 | title('Red'); 30 | 31 | % Create subplot 32 | subplot2 = subplot(2,2,2,'Parent',figure1,'YDir','reverse','Layer','top'); 33 | % Uncomment the following line to preserve the X-limits of the axes 34 | xlim([0.5 320.5]); 35 | % Uncomment the following line to preserve the Y-limits of the axes 36 | ylim([0.5 240.5]); 37 | box('on'); 38 | hold('all'); 39 | 40 | % Create title 41 | title('Green'); 42 | 43 | % Create image 44 | image(green,'Parent',subplot2,'CDataMapping','scaled'); 45 | 46 | % Create subplot 47 | subplot3 = subplot(2,2,3,'Parent',figure1,'YDir','reverse','Layer','top'); 48 | % Uncomment the following line to preserve the X-limits of the axes 49 | xlim([0.5 320.5]); 50 | % Uncomment the following line to preserve the Y-limits of the axes 51 | ylim([0.5 240.5]); 52 | box('on'); 53 | hold('all'); 54 | 55 | % Create title 56 | title('Blue'); 57 | 58 | % Create image 59 | image(blue,'Parent',subplot3,'CDataMapping','scaled'); 60 | 61 | % Create subplot 62 | subplot4 = subplot(2,2,4,'Parent',figure1,'YDir','reverse','Layer','top'); 63 | % Uncomment the following line to preserve the X-limits of the axes 64 | xlim([0.5 320.5]); 65 | % Uncomment the following line to preserve the Y-limits of the axes 66 | ylim([0.5 240.5]); 67 | box('on'); 68 | hold('all'); 69 | 70 | % Create title 71 | title('Just Green'); 72 | 73 | % Create image 74 | image(justGreen,'Parent',subplot4,'CDataMapping','scaled'); 75 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end 13 | end 14 | 15 | Imgabout = conv2(I,double(imag(G)),'same'); 16 | Regabout = conv2(I,double(real(G)),'same'); 17 | 18 | gabout = sqrt(Imgabout.*Imgabout + Regabout.*Regabout); -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载/第17章/人足迹特征比对系统设计/gaborfilter_pic.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codes-books/matlab-optimization-advanced/31bd3a38557ddd17e366849bdd83262020e7ae42/MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载/第17章/人足迹特征比对系统设计/gaborfilter_pic.m -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载/第17章/人足迹特征比对系统设计/kirsch_algorithm.m: -------------------------------------------------------------------------------- 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den=sqrt((r-g).*(r-g))+(r-b).*(g-b); 9 | theta=acos(num./(den+eps)); 10 | 11 | H=theta; 12 | H(b>g)=2*pi-H(b>g); 13 | H=H/(2*pi); 14 | 15 | num=min(min(r,g),b); 16 | den=r+g+b; 17 | den(den==0)=eps; 18 | S=1-3.*num./den; 19 | H(S==0)=0; 20 | 21 | I=(r+g+b)/3; 22 | 23 | hsi=cat(3,H,S,I); 24 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载/第17章/人足迹特征比对系统设计/robetrs_seg.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codes-books/matlab-optimization-advanced/31bd3a38557ddd17e366849bdd83262020e7ae42/MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载/第17章/人足迹特征比对系统设计/robetrs_seg.m -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）11-20章程序下载/第17章/人足迹特征比对系统设计/sobel_seg.m: -------------------------------------------------------------------------------- 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',num2str(flag)]); 11 | end 12 | function [sys,x0,str,ts]=mdlInitializeSizes 13 | sizes = simsizes; 14 | sizes.NumContStates = 0; 15 | sizes.NumDiscStates = 0; 16 | sizes.NumOutputs = 3; 17 | sizes.NumInputs = 3; 18 | sizes.DirFeedthrough = 1; 19 | sizes.NumSampleTimes = 0; 20 | sys = simsizes(sizes); 21 | x0 = []; 22 | str = []; 23 | ts = []; 24 | function sys=mdlOutputs(t,x,u) 25 | thd=u(1); 26 | dthd=cos(t); 27 | ddthd=-sin(t); 28 | 29 | th=u(2); 30 | dth=u(3); 31 | 32 | c=15; 33 | e=thd-th; 34 | de=dthd-dth; 35 | s=c*e+de; 36 | 37 | fx=25*dth; 38 | b=133; 39 | 40 | epc=5;k=10; 41 | ut=1/b*(epc*sign(s)+k*s+c*de+ddthd+fx); 42 | 43 | sys(1)=ut; 44 | sys(2)=e; 45 | sys(3)=de; -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw1plant.m: -------------------------------------------------------------------------------- 1 | function [sys,x0,str,ts]=s_function(t,x,u,flag) 2 | switch flag, 3 | case 0, 4 | [sys,x0,str,ts]=mdlInitializeSizes; 5 | case 1, 6 | sys=mdlDerivatives(t,x,u); 7 | case 3, 8 | sys=mdlOutputs(t,x,u); 9 | case {2, 4, 9 } 10 | sys = []; 11 | otherwise 12 | error(['Unhandled flag = ',num2str(flag)]); 13 | end 14 | function [sys,x0,str,ts]=mdlInitializeSizes 15 | sizes = simsizes; 16 | sizes.NumContStates = 2; 17 | sizes.NumDiscStates = 0; 18 | sizes.NumOutputs = 2; 19 | sizes.NumInputs = 1; 20 | sizes.DirFeedthrough = 0; 21 | sizes.NumSampleTimes = 0; 22 | sys=simsizes(sizes); 23 | x0=[-0.15 -0.15]; 24 | str=[]; 25 | ts=[]; 26 | function sys=mdlDerivatives(t,x,u) 27 | sys(1)=x(2); 28 | sys(2)=-25*x(2)+133*u; 29 | function sys=mdlOutputs(t,x,u) 30 | sys(1)=x(1); 31 | sys(2)=x(2); -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw1plot.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codes-books/matlab-optimization-advanced/31bd3a38557ddd17e366849bdd83262020e7ae42/MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw1plot.m -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw2ctrl.m: -------------------------------------------------------------------------------- 1 | function [sys,x0,str,ts] = spacemodel(t,x,u,flag) 2 | switch flag, 3 | case 0, 4 | [sys,x0,str,ts]=mdlInitializeSizes; 5 | case 3, 6 | sys=mdlOutputs(t,x,u); 7 | case {2,4,9} 8 | sys=[]; 9 | otherwise 10 | error(['Unhandled flag = ',num2str(flag)]); 11 | end 12 | 13 | function [sys,x0,str,ts]=mdlInitializeSizes 14 | sizes = simsizes; 15 | sizes.NumContStates = 0; 16 | sizes.NumDiscStates = 0; 17 | sizes.NumOutputs = 1; 18 | sizes.NumInputs = 4; 19 | sizes.DirFeedthrough = 1; 20 | sizes.NumSampleTimes = 1; 21 | sys = simsizes(sizes); 22 | x0 = []; 23 | str = []; 24 | ts = [0 0]; 25 | 26 | function sys=mdlOutputs(t,x,u) 27 | x1=u(1);x2=u(2); %Pendulum angle 28 | x3=u(3);x4=u(4); %Cart 29 | 30 | g=9.8;mc=0.5;m=0.5;l=0.3; 31 | alfaa=1; 32 | 33 | %-1,-2,-3 34 | alfau=1+g/(11*l); 35 | nmna=6*l/g*(alfau-1); 36 | nmnu=nmna+6*(alfau-1); 37 | 38 | s=alfaa*x4+nmna*x3+l*alfau*x2+l*nmnu*x1; 39 | sr=nmna*x3+l*nmnu*x1; 40 | dsr=nmna*x4+l*nmnu*x2; 41 | 42 | Maa=mc+m; 43 | Mau=m*l*cos(x1); 44 | Muu=m*l^2; 45 | fa=m*l*sin(x1)*x2^2; 46 | fu=m*g*l*sin(x1); 47 | 48 | xite=3; 49 | fai=0.10; 50 | if abs(s)<=fai 51 | sat=s/fai; 52 | else 53 | sat=sign(s); 54 | end 55 | 56 | b1=Muu/(Maa*Muu-Mau^2); 57 | b2=-Mau/(Maa*Muu-Mau^2); 58 | 59 | f1=(Muu*fa-Mau*fu)/(Maa*Muu-Mau^2); 60 | f2=(-Mau*fa+Maa*fu)/(Maa*Muu-Mau^2); 61 | ut=-1/(alfaa*b1+l*alfau*b2)*(alfaa*f1+l*alfau*f2+dsr+xite*sat); 62 | sys(1)=ut; -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw2ctrl_sine.m: -------------------------------------------------------------------------------- 1 | function [sys,x0,str,ts] = spacemodel(t,x,u,flag) 2 | switch flag, 3 | case 0, 4 | [sys,x0,str,ts]=mdlInitializeSizes; 5 | case 3, 6 | sys=mdlOutputs(t,x,u); 7 | case {2,4,9} 8 | sys=[]; 9 | otherwise 10 | error(['Unhandled flag = ',num2str(flag)]); 11 | end 12 | 13 | function [sys,x0,str,ts]=mdlInitializeSizes 14 | sizes = simsizes; 15 | sizes.NumContStates = 0; 16 | sizes.NumDiscStates = 0; 17 | sizes.NumOutputs = 1; 18 | sizes.NumInputs = 5; 19 | sizes.DirFeedthrough = 1; 20 | sizes.NumSampleTimes = 1; 21 | sys = simsizes(sizes); 22 | x0 = []; 23 | str = []; 24 | ts = [0 0]; 25 | 26 | function sys=mdlOutputs(t,x,u) 27 | 28 | x1=u(2);x2=u(3); %Pendulum angle 29 | x3=u(4);x4=u(5); %Cart 30 | 31 | g=9.8;mc=0.5;m=0.5;l=0.3; 32 | alfaa=1; 33 | 34 | %-1,-2,-3 35 | alfau=1+g/(11*l); 36 | nmna=6*l/g*(alfau-1); 37 | nmnu=nmna+6*(alfau-1); 38 | 39 | s=alfaa*x4+nmna*x3+l*alfau*x2+l*nmnu*x1; 40 | sr=nmna*x3+l*nmnu*x1; 41 | dsr=nmna*x4+l*nmnu*x2; 42 | 43 | Maa=mc+m; 44 | Mau=m*l*cos(x1); 45 | Muu=m*l^2; 46 | fa=m*l*sin(x1)*x2^2; 47 | fu=m*g*l*sin(x1); 48 | 49 | xite=3; 50 | fai=0.10*u(1); 51 | if abs(s)<=fai 52 | sat=s/fai; 53 | else 54 | sat=sign(s); 55 | end 56 | 57 | b1=Muu/(Maa*Muu-Mau^2); 58 | b2=-Mau/(Maa*Muu-Mau^2); 59 | 60 | f1=(Muu*fa-Mau*fu)/(Maa*Muu-Mau^2); 61 | f2=(-Mau*fa+Maa*fu)/(Maa*Muu-Mau^2); 62 | ut=-1/(alfaa*b1+l*alfau*b2)*(alfaa*f1+l*alfau*f2+dsr+xite*sat); 63 | sys(1)=ut; 64 | 65 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw2plant.m: -------------------------------------------------------------------------------- 1 | function [sys,x0,str,ts] = spacemodel(t,x,u,flag) 2 | switch flag, 3 | case 0, 4 | [sys,x0,str,ts]=mdlInitializeSizes; 5 | case 1, 6 | sys=mdlDerivatives(t,x,u); 7 | case 3, 8 | sys=mdlOutputs(t,x,u); 9 | case {2,4,9} 10 | sys=[]; 11 | otherwise 12 | error(['Unhandled flag = ',num2str(flag)]); 13 | end 14 | 15 | function [sys,x0,str,ts]=mdlInitializeSizes 16 | sizes = simsizes; 17 | sizes.NumContStates = 4; 18 | sizes.NumDiscStates = 0; 19 | sizes.NumOutputs = 4; 20 | sizes.NumInputs = 1; 21 | sizes.DirFeedthrough = 0; 22 | sizes.NumSampleTimes = 0; 23 | sys = simsizes(sizes); 24 | x0 =[-pi/3,0,0.5,0]; 25 | str = []; 26 | ts = []; 27 | 28 | function sys=mdlDerivatives(t,x,u) 29 | g=9.8;mc=0.5;m=0.5;l=0.3; 30 | th=x(1); 31 | dth=x(2); 32 | 33 | M=[mc+m m*l*cos(th); 34 | m*l*cos(th) m*l^2]; 35 | fa=m*l*sin(th)*dth^2; 36 | fu=m*g*l*sin(th); 37 | F=[fa;fu]; 38 | 39 | U=[u(1);0]; 40 | 41 | a=inv(M)*(F+U); 42 | ddx=a(1); 43 | ddth=a(2); 44 | 45 | sys(1)=x(2); 46 | sys(2)=ddth; %Pendulum angle 47 | sys(3)=x(4); 48 | sys(4)=ddx; %Cart 49 | 50 | function sys=mdlOutputs(t,x,u) 51 | sys(1)=x(1); 52 | sys(2)=x(2); 53 | sys(3)=x(3); 54 | sys(4)=x(4); 55 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw2plant_sine.m: -------------------------------------------------------------------------------- 1 | function [sys,x0,str,ts] = spacemodel(t,x,u,flag) 2 | switch flag, 3 | case 0, 4 | [sys,x0,str,ts]=mdlInitializeSizes; 5 | case 1, 6 | sys=mdlDerivatives(t,x,u); 7 | case 3, 8 | sys=mdlOutputs(t,x,u); 9 | case {2,4,9} 10 | sys=[]; 11 | otherwise 12 | error(['Unhandled flag = ',num2str(flag)]); 13 | end 14 | 15 | function [sys,x0,str,ts]=mdlInitializeSizes 16 | sizes = simsizes; 17 | sizes.NumContStates = 4; 18 | sizes.NumDiscStates = 0; 19 | sizes.NumOutputs = 4; 20 | sizes.NumInputs = 1; 21 | sizes.DirFeedthrough = 0; 22 | sizes.NumSampleTimes = 0; 23 | sys = simsizes(sizes); 24 | x0 =[-pi/3,0,0.5,0]; 25 | str = []; 26 | ts = []; 27 | 28 | function sys=mdlDerivatives(t,x,u) 29 | g=9.8;mc=0.5;m=0.5;l=0.3; 30 | th=x(1); 31 | dth=x(2); 32 | 33 | M=[mc+m m*l*cos(th); 34 | m*l*cos(th) m*l^2]; 35 | fa=m*l*sin(th)*dth^2; 36 | fu=m*g*l*sin(th); 37 | F=[fa;fu]; 38 | 39 | U=[u(1);0]; 40 | 41 | a=inv(M)*(F+U); 42 | ddx=a(1); 43 | ddth=a(2); 44 | 45 | sys(1)=x(2); 46 | sys(2)=ddth; %Pendulum angle 47 | sys(3)=x(4); 48 | sys(4)=ddx; %Cart 49 | 50 | function sys=mdlOutputs(t,x,u) 51 | sys(1)=x(1); 52 | sys(2)=x(2); 53 | sys(3)=x(3); 54 | sys(4)=x(4); 55 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw2plot.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codes-books/matlab-optimization-advanced/31bd3a38557ddd17e366849bdd83262020e7ae42/MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw2plot.m -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第22章/ysw2plot_sine.m: -------------------------------------------------------------------------------- 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MaxMDLFileLineLength 120 23 | Created "Sun Sep 03 10:58:19 2000" 24 | Creator "ljk" 25 | UpdateHistory "UpdateHistoryNever" 26 | ModifiedByFormat "%" 27 | LastModifiedBy "ljk" 28 | ModifiedDateFormat "%" 29 | LastModifiedDate "Tue Apr 17 09:34:03 2012" 30 | RTWModifiedTimeStamp 256556039 31 | ModelVersionFormat "1.%" 32 | ConfigurationManager "none" 33 | SampleTimeColors off 34 | SampleTimeAnnotations off 35 | LibraryLinkDisplay "none" 36 | WideLines off 37 | ShowLineDimensions off 38 | ShowPortDataTypes off 39 | ShowLoopsOnError on 40 | IgnoreBidirectionalLines off 41 | ShowStorageClass off 42 | ShowTestPointIcons on 43 | ShowSignalResolutionIcons on 44 | ShowViewerIcons on 45 | SortedOrder off 46 | ExecutionContextIcon off 47 | ShowLinearizationAnnotations on 48 | BlockNameDataTip off 49 | BlockParametersDataTip on 50 | BlockDescriptionStringDataTip off 51 | ToolBar on 52 | StatusBar on 53 | BrowserShowLibraryLinks off 54 | BrowserLookUnderMasks off 55 | SimulationMode "normal" 56 | LinearizationMsg "none" 57 | Profile off 58 | ParamWorkspaceSource "MATLABWorkspace" 59 | AccelSystemTargetFile "accel.tlc" 60 | AccelTemplateMakefile "accel_default_tmf" 61 | AccelMakeCommand "make_rtw" 62 | TryForcingSFcnDF off 63 | RecordCoverage off 64 | CovPath "/" 65 | CovSaveName "covdata" 66 | CovMetricSettings "dw" 67 | CovNameIncrementing off 68 | CovHtmlReporting on 69 | CovForceBlockReductionOff on 70 | covSaveCumulativeToWorkspaceVar on 71 | CovSaveSingleToWorkspaceVar on 72 | CovCumulativeVarName "covCumulativeData" 73 | CovCumulativeReport off 74 | CovReportOnPause on 75 | CovModelRefEnable "Off" 76 | CovExternalEMLEnable off 77 | ExtModeBatchMode off 78 | ExtModeEnableFloating on 79 | ExtModeTrigType "manual" 80 | ExtModeTrigMode "oneshot" 81 | ExtModeTrigPort "1" 82 | ExtModeTrigElement "any" 83 | ExtModeTrigDuration 1000 84 | ExtModeTrigDurationFloating "auto" 85 | ExtModeTrigHoldOff 0 86 | ExtModeTrigDelay 0 87 | ExtModeTrigDirection "rising" 88 | ExtModeTrigLevel 0 89 | ExtModeArchiveMode "off" 90 | ExtModeAutoIncOneShot off 91 | ExtModeIncDirWhenArm off 92 | ExtModeAddSuffixToVar off 93 | ExtModeWriteAllDataToWs off 94 | ExtModeArmWhenConnect off 95 | ExtModeSkipDownloadWhenConnect off 96 | ExtModeLogAll on 97 | ExtModeAutoUpdateStatusClock on 98 | BufferReuse on 99 | ShowModelReferenceBlockVersion off 100 | ShowModelReferenceBlockIO off 101 | Array { 102 | Type "Handle" 103 | Dimension 1 104 | Simulink.ConfigSet { 105 | $ObjectID 1 106 | Version "1.11.0" 107 | Array { 108 | Type "Handle" 109 | Dimension 8 110 | Simulink.SolverCC { 111 | $ObjectID 2 112 | Version "1.11.0" 113 | StartTime "0.0" 114 | StopTime "50" 115 | AbsTol "auto" 116 | FixedStep "0.001" 117 | InitialStep "auto" 118 | MaxNumMinSteps "-1" 119 | MaxOrder 5 120 | ZcThreshold "auto" 121 | ConsecutiveZCsStepRelTol "10*128*eps" 122 | MaxConsecutiveZCs "1000" 123 | ExtrapolationOrder 4 124 | NumberNewtonIterations 1 125 | MaxStep "auto" 126 | MinStep "auto" 127 | MaxConsecutiveMinStep "1" 128 | RelTol "1e-3" 129 | SolverMode "SingleTasking" 130 | ConcurrentTasks off 131 | Solver "ode45" 132 | SolverName "ode45" 133 | SolverJacobianMethodControl "auto" 134 | ShapePreserveControl "DisableAll" 135 | ZeroCrossControl "UseLocalSettings" 136 | ZeroCrossAlgorithm "Nonadaptive" 137 | AlgebraicLoopSolver "TrustRegion" 138 | SolverResetMethod "Fast" 139 | PositivePriorityOrder off 140 | AutoInsertRateTranBlk off 141 | SampleTimeConstraint "Unconstrained" 142 | InsertRTBMode "Whenever possible" 143 | } 144 | Simulink.DataIOCC { 145 | $ObjectID 3 146 | Version "1.11.0" 147 | Decimation "1" 148 | ExternalInput "[t, u]" 149 | FinalStateName "xFinal" 150 | InitialState "xInitial" 151 | LimitDataPoints off 152 | MaxDataPoints "1000" 153 | LoadExternalInput off 154 | LoadInitialState off 155 | SaveFinalState off 156 | SaveCompleteFinalSimState off 157 | SaveFormat "Array" 158 | SignalLoggingSaveFormat "ModelDataLogs" 159 | SaveOutput on 160 | SaveState off 161 | SignalLogging on 162 | DSMLogging on 163 | InspectSignalLogs off 164 | SaveTime on 165 | ReturnWorkspaceOutputs off 166 | StateSaveName "xout" 167 | TimeSaveName "tout" 168 | OutputSaveName "yout" 169 | SignalLoggingName "sigsOut" 170 | DSMLoggingName "dsmout" 171 | OutputOption "RefineOutputTimes" 172 | OutputTimes "[]" 173 | ReturnWorkspaceOutputsName "out" 174 | Refine "1" 175 | } 176 | Simulink.OptimizationCC { 177 | $ObjectID 4 178 | Version "1.11.0" 179 | Array { 180 | Type "Cell" 181 | Dimension 8 182 | Cell "BooleansAsBitfields" 183 | Cell "PassReuseOutputArgsAs" 184 | Cell "PassReuseOutputArgsThreshold" 185 | Cell "ZeroExternalMemoryAtStartup" 186 | Cell "ZeroInternalMemoryAtStartup" 187 | Cell "OptimizeModelRefInitCode" 188 | Cell "NoFixptDivByZeroProtection" 189 | Cell "UseSpecifiedMinMax" 190 | PropName "DisabledProps" 191 | } 192 | BlockReduction on 193 | BooleanDataType off 194 | ConditionallyExecuteInputs on 195 | InlineParams off 196 | UseIntDivNetSlope off 197 | 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| BusNameAdapt "WarnAndRepair" 308 | NonBusSignalsTreatedAsBus "none" 309 | LoggingUnavailableSignals "error" 310 | BlockIODiagnostic "none" 311 | SFUnusedDataAndEventsDiag "warning" 312 | SFUnexpectedBacktrackingDiag "warning" 313 | SFInvalidInputDataAccessInChartInitDiag "warning" 314 | SFNoUnconditionalDefaultTransitionDiag "warning" 315 | SFTransitionOutsideNaturalParentDiag "warning" 316 | } 317 | Simulink.HardwareCC { 318 | $ObjectID 6 319 | Version "1.11.0" 320 | ProdBitPerChar 8 321 | ProdBitPerShort 16 322 | ProdBitPerInt 32 323 | ProdBitPerLong 32 324 | ProdBitPerFloat 32 325 | ProdBitPerDouble 64 326 | ProdBitPerPointer 32 327 | ProdLargestAtomicInteger "Char" 328 | ProdLargestAtomicFloat "None" 329 | ProdIntDivRoundTo "Undefined" 330 | ProdEndianess "Unspecified" 331 | ProdWordSize 32 332 | ProdShiftRightIntArith on 333 | ProdHWDeviceType "32-bit Generic" 334 | TargetBitPerChar 8 335 | TargetBitPerShort 16 336 | TargetBitPerInt 32 337 | TargetBitPerLong 32 338 | 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ModelReferenceMinAlgLoopOccurrences off 366 | PropagateSignalLabelsOutOfModel off 367 | SupportModelReferenceSimTargetCustomCode off 368 | } 369 | Simulink.SFSimCC { 370 | $ObjectID 8 371 | Version "1.11.0" 372 | SFSimEnableDebug on 373 | SFSimOverflowDetection on 374 | SFSimEcho on 375 | SimBlas on 376 | SimCtrlC on 377 | SimExtrinsic on 378 | SimIntegrity on 379 | SimUseLocalCustomCode off 380 | SimParseCustomCode on 381 | SimBuildMode "sf_incremental_build" 382 | } 383 | Simulink.RTWCC { 384 | $BackupClass "Simulink.RTWCC" 385 | $ObjectID 9 386 | Version "1.11.0" 387 | Array { 388 | Type "Cell" 389 | Dimension 8 390 | Cell "IncludeHyperlinkInReport" 391 | Cell "GenerateTraceInfo" 392 | Cell "GenerateTraceReport" 393 | Cell "GenerateTraceReportSl" 394 | Cell "GenerateTraceReportSf" 395 | Cell "GenerateTraceReportEml" 396 | Cell "GenerateSLWebview" 397 | Cell "GenerateCodeMetricsReport" 398 | PropName "DisabledProps" 399 | } 400 | SystemTargetFile "grt.tlc" 401 | GenCodeOnly off 402 | 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MultiwordLength 2048 537 | GenerateFullHeader on 538 | GenerateSampleERTMain off 539 | GenerateTestInterfaces off 540 | IsPILTarget off 541 | ModelReferenceCompliant on 542 | ParMdlRefBuildCompliant on 543 | CompOptLevelCompliant on 544 | IncludeMdlTerminateFcn on 545 | GeneratePreprocessorConditionals "Disable all" 546 | CombineOutputUpdateFcns off 547 | CombineSignalStateStructs off 548 | SuppressErrorStatus off 549 | ERTFirstTimeCompliant off 550 | IncludeFileDelimiter "Auto" 551 | ERTCustomFileBanners off 552 | SupportAbsoluteTime on 553 | LogVarNameModifier "rt_" 554 | MatFileLogging on 555 | MultiInstanceERTCode off 556 | SupportNonFinite on 557 | SupportComplex on 558 | PurelyIntegerCode off 559 | SupportContinuousTime on 560 | SupportNonInlinedSFcns on 561 | SupportVariableSizeSignals off 562 | EnableShiftOperators on 563 | ParenthesesLevel "Nominal" 564 | PortableWordSizes off 565 | ModelStepFunctionPrototypeControlCompliant off 566 | CPPClassGenCompliant off 567 | AutosarCompliant off 568 | UseMalloc off 569 | ExtMode off 570 | ExtModeStaticAlloc off 571 | ExtModeTesting off 572 | ExtModeStaticAllocSize 1000000 573 | ExtModeTransport 0 574 | ExtModeMexFile "ext_comm" 575 | ExtModeIntrfLevel "Level1" 576 | RTWCAPISignals off 577 | RTWCAPIParams off 578 | RTWCAPIStates off 579 | RTWCAPIRootIO off 580 | GenerateASAP2 off 581 | } 582 | PropName "Components" 583 | } 584 | } 585 | PropName "Components" 586 | } 587 | Name "Configuration" 588 | CurrentDlgPage "Solver" 589 | ConfigPrmDlgPosition [ 200, 85, 1080, 715 ] 590 | } 591 | PropName "ConfigurationSets" 592 | } 593 | Simulink.ConfigSet { 594 | $PropName "ActiveConfigurationSet" 595 | $ObjectID 1 596 | } 597 | BlockDefaults { 598 | ForegroundColor "black" 599 | BackgroundColor "white" 600 | DropShadow off 601 | NamePlacement "normal" 602 | FontName "Helvetica" 603 | FontSize 10 604 | FontWeight "normal" 605 | FontAngle "normal" 606 | ShowName on 607 | BlockRotation 0 608 | BlockMirror off 609 | } 610 | AnnotationDefaults { 611 | HorizontalAlignment "center" 612 | VerticalAlignment "middle" 613 | ForegroundColor "black" 614 | BackgroundColor "white" 615 | DropShadow off 616 | FontName "Helvetica" 617 | FontSize 10 618 | FontWeight "normal" 619 | FontAngle "normal" 620 | UseDisplayTextAsClickCallback off 621 | } 622 | LineDefaults { 623 | FontName "Helvetica" 624 | FontSize 9 625 | FontWeight "normal" 626 | FontAngle "normal" 627 | } 628 | BlockParameterDefaults { 629 | Block { 630 | BlockType Clock 631 | DisplayTime off 632 | } 633 | Block { 634 | BlockType S-Function 635 | FunctionName "system" 636 | SFunctionModules "''" 637 | PortCounts "[]" 638 | SFunctionDeploymentMode off 639 | } 640 | Block { 641 | BlockType ToWorkspace 642 | VariableName "simulink_output" 643 | MaxDataPoints "1000" 644 | Decimation "1" 645 | SampleTime "0" 646 | FixptAsFi off 647 | NumInputs "1" 648 | } 649 | } 650 | System { 651 | Name "chap5_7sim" 652 | Location [2, 93, 1900, 1038] 653 | Open on 654 | 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-------------------------------------------------------------------------------- 1 | function ObjVal = ackley(Chrom,switch1); 2 | 3 | Dim=size(Chrom,2); 4 | 5 | % Compute population parameters 6 | [Nind,Nvar] = size(Chrom); 7 | 8 | 9 | A = 1/Dim; 10 | Omega = 2 * pi; 11 | sum1=A.*sum((Chrom .* Chrom)')'; 12 | %sum1=A.*sum(Chrom .* Chrom); 13 | sum2=A.*sum((cos(Omega * Chrom))')'; 14 | ObjVal = -20*exp(-0.2*sqrt(sum1))-exp(sum2)+20+exp(1); 15 | 16 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第27章/calculateFitness.m: -------------------------------------------------------------------------------- 1 | function fFitness=calculateFitness(fObjV) 2 | fFitness=zeros(size(fObjV)); 3 | ind=find(fObjV>=0); 4 | fFitness(ind)=1./(fObjV(ind)+1); 5 | ind=find(fObjV<0); 6 | fFitness(ind)=1+abs(fObjV(ind)); 7 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第27章/griewank.m: -------------------------------------------------------------------------------- 1 | function ObjVal = griewank(Chrom,switch1); 2 | [Nind, Nvar] = size(Chrom); 3 | nummer = repmat(1:Nvar, [Nind 1]); 4 | ObjVal = sum(((Chrom.^2) / 4000)')' - prod(cos(Chrom ./ sqrt(nummer))')' + 1; 5 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第27章/rastrigin.m: -------------------------------------------------------------------------------- 1 | 2 | function ObjVal = rastrigin(Chrom,switch1); 3 | 4 | % Dimension of objective function 5 | Dim=size(Chrom,2); 6 | 7 | % Compute population parameters 8 | [Nind,Nvar] = size(Chrom); 9 | 10 | % function 6, Dim*A + sum of (xi^2 - A*cos(Omega*xi)) for i = 1:Dim (Dim=20) 11 | % n = Dim, -5.12 <= xi <= 5.12 12 | % global minimum at (xi)=(0) ; fmin=0 13 | A = 10; 14 | Omega = 2 * pi; 15 | ObjVal = Dim * A + sum(((Chrom .* Chrom) - A * cos(Omega * Chrom))')'; 16 | % ObjVal = diag(Chrom * Chrom'); % both lines produce the same 17 | % otherwise error, wrong format of Chrom 18 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第27章/rosenbrock.m: -------------------------------------------------------------------------------- 1 | function ObjVal = rosenbrock(Chrom,switc); 2 | 3 | % Dimension of objective function 4 | 5 | Dim=size(Chrom,2); 6 | 7 | % Compute population parameters 8 | [Nind,Nvar] = size(Chrom); 9 | 10 | 11 | % function 11, sum of 100* (x(i+1) -xi^2)^2+(1-xi)^2 for i = 1:Dim (Dim=10) 12 | % n = Dim, -10 <= xi <= 10 13 | % global minimum at (xi)=(1) ; fmin=0 14 | Mat1 = Chrom(:,1:Nvar-1); 15 | Mat2 = Chrom(:,2:Nvar); 16 | 17 | if Dim == 2 18 | ObjVal = 100*(Mat2-Mat1.^2).^2+(1-Mat1).^2; 19 | else 20 | ObjVal = sum((100*(Mat2-Mat1.^2).^2+(1-Mat1).^2)')'; 21 | end 22 | 23 | 24 | % End of function 25 | 26 | 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(y+1).^2) ... 6 | - 10*(x/5 - x.^3 - y.^5).*exp(-x.^2-y.^2) ... 7 | - 1/3*exp(-(x+1).^2 - y.^2); 8 | end 9 | 10 | -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第30章/AF_init.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codes-books/matlab-optimization-advanced/31bd3a38557ddd17e366849bdd83262020e7ae42/MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第30章/AF_init.m -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第30章/AF_prey.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codes-books/matlab-optimization-advanced/31bd3a38557ddd17e366849bdd83262020e7ae42/MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第30章/AF_prey.m -------------------------------------------------------------------------------- /MATLAB优化算法案例分析与应用（进阶篇）21-30章程序下载/第30章/AF_swarm.m: 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