├── AHP ├── CalculationRI.m ├── ahp.m ├── data.txt ├── main.m ├── sglsortexamine.m └── tolsortvec.m ├── CellularAutomata ├── 初等元胞自动机 │ └── basic_CA.m ├── 扩散限制聚集 │ └── main.m ├── 森林火灾 │ ├── foreset_fire.m │ └── main.m ├── 气体动力学 │ └── main.m ├── 渗流集群 │ └── main.m ├── 激发介质 │ └── main.m ├── 生命游戏 │ ├── game_of_life.m │ └── main.m ├── 砂堆规则 │ └── main.m └── 表面张力 │ └── main.m ├── FuzzyMathematicalModel ├── 多层次模糊综合评价 │ ├── data.txt │ └── main.m ├── 多目标模糊综合评价 │ ├── main.m │ └── muti_objective_fuzzy_analysis.m └── 模糊聚类 │ ├── Riemann_closeness.m │ ├── caculate_SSE.m │ ├── data.txt │ ├── data1.mat │ ├── fuzzy_cluster_analysis.m │ ├── fuzzy_matrix_compund.m │ ├── fuzzy_pattern_recognition.m │ ├── main.m │ ├── maximun.m │ └── minimun.m ├── GoalProgramming ├── fun.m └── main.m ├── GraphTheory ├── basic │ ├── grBase.m │ ├── grCoBase.m │ ├── grCoCycleBasis.m │ ├── grColEdge.m │ ├── grColVer.m │ ├── grComp.m │ ├── grCycleBasis.m │ ├── grDecOrd.m │ ├── grDistances.m │ ├── grEccentricity.m │ ├── grIsEulerian.m │ ├── grMaxComSu.m │ ├── grMaxFlows.m │ ├── grMaxMatch.m │ ├── grMaxStabSet.m │ ├── grMinAbsEdgeSet.m │ ├── grMinAbsVerSet.m │ ├── grMinCutSet.m │ ├── grMinEdgeCover.m │ ├── grMinSpanTree.m │ ├── grMinVerCover.m │ ├── grPERT.m │ ├── grPlot.m │ ├── grShortPath.m │ ├── grTheoryTest.m │ ├── grTravSale.m │ ├── grValidation.m │ └── readme.txt ├── detailed │ ├── Euler图和Hamilton图 │ │ ├── Fleuf1.m │ │ ├── edf.m │ │ ├── flecvexf.m │ │ ├── glf.m │ │ └── readme.txt │ ├── 匹配问题 │ │ ├── fc01.asv │ │ ├── fc01.m │ │ ├── fc02.asv │ │ ├── fc02.m │ │ ├── fc03.asv │ │ ├── fc03.m │ │ ├── matgraf.asv │ │ ├── matgraf.m │ │ └── readme.txt │ ├── 图的染色 │ │ ├── colorcodf.asv │ │ ├── colorcodf.m │ │ ├── edgecodf.asv │ │ ├── edgecodf.m │ │ ├── graphcodf.asv │ │ ├── graphcodf.m │ │ ├── graphdifcodf.asv │ │ ├── graphdiscodf.m │ │ ├── graphunicodf.asv │ │ ├── graphunicodf.m │ │ └── readme.txt │ ├── 最小费用流 │ │ ├── BGf.asv │ │ └── BGf.m │ ├── 最短路 │ │ ├── Dijkf.m │ │ ├── Floyd.m │ │ ├── cn2shortf.m │ │ ├── efpathf.m │ │ ├── n2shortf.m │ │ ├── p_pathf.m │ │ ├── readme.txt │ │ ├── shorp2f.m │ │ └── shortdf.m │ ├── 树 │ │ ├── BFS.asv │ │ ├── BFS.m │ │ ├── DFS.asv │ │ ├── DFS.m │ │ ├── DFS1.asv │ │ ├── Directory.asv │ │ ├── Huffman.asv │ │ ├── Huffman.m │ │ ├── inoutgraf.asv │ │ ├── inoutgraf.m │ │ ├── readme.txt │ │ ├── treedgraf.m │ │ ├── treegraf.asv │ │ └── treegraf.m │ ├── 网络流 │ │ ├── boundnetf.asv │ │ ├── boundnetf.m │ │ ├── fofuf.asv │ │ ├── fofuf.m │ │ ├── readme.txt │ │ ├── restrf.asv │ │ └── restrf.m │ └── 连通图 │ │ ├── centgraf.m │ │ ├── concom.m │ │ ├── readme.txt │ │ └── ucengraf.m ├── dijkstra求解最短路径 │ ├── Dijkstra.in │ ├── Dijkstra.out │ ├── dijkstra.cpp │ └── dijkstra.exe └── floyd求解最短路径 │ ├── APSP.in │ ├── APSP.out │ ├── floyd.cpp │ └── floyd.exe ├── GreySystem ├── GM_1_1.m ├── GM_1_1_full_procession.m ├── GM_2_1.m ├── GM_Verhulst.m ├── GM_full.m ├── association_analysis.m ├── example_1.txt ├── example_2.txt └── strength_analysis.m ├── HeuristicAlgorithm ├── 模拟退火算法 │ ├── 10_cities_TSP │ │ ├── distance.m │ │ ├── main.m │ │ ├── myplot.m │ │ ├── pathfare.m │ │ └── swap.m │ ├── TSP(SA) │ │ ├── Final Solution.pdf │ │ ├── README.txt │ │ ├── china.mat │ │ ├── distancematrix.m │ │ ├── fpdfprinter.m │ │ ├── license.txt │ │ ├── main.m │ │ ├── perturb.m │ │ ├── plotcities.m │ │ ├── plotroute.m │ │ └── totaldistance.m │ ├── example_1.txt │ └── simulated_annealing.m ├── 神经网络算法 │ └── MATLAB神经网络30个案例分析 │ │ ├── [教材]MATLAB神经网络30个案例分析.pdf │ │ ├── 案例1 BP神经网络的数据分类-语音特征信号分类 │ │ ├── BP.m │ │ ├── BPDLX.m │ │ ├── data1.mat │ │ ├── data2.mat │ │ ├── data3.mat │ │ └── data4.mat │ │ ├── 案例10 离散Hopfield神经网络的分类——高校科研能力评价 │ │ ├── chapter10.m │ │ ├── class.mat │ │ ├── sim.mat │ │ ├── stdlib.m │ │ └── test.m │ │ ├── 案例11 连续Hopfield神经网络的优化-旅行商问题优化计算 │ │ ├── city_location.mat │ │ ├── diff_u.m │ │ ├── energy.m │ │ └── main.m │ │ ├── 案例12 SVM神经网络的数据分类预测-葡萄酒种类识别 │ │ ├── chapter12.m │ │ ├── chapter12_wine.mat │ │ └── html │ │ │ ├── chapter12.html │ │ │ ├── chapter12.png │ │ │ ├── chapter12_01.png │ │ │ ├── chapter12_02.png │ │ │ └── chapter12_03.png │ │ ├── 案例13 SVM神经网络中的参数优化---提升分类器性能 │ │ ├── chapter13_GA.m │ │ ├── chapter13_GridSearch.m │ │ ├── chapter13_PSO.m │ │ ├── chapter13_wine.mat │ │ └── html │ │ │ ├── chapter13.html │ │ │ ├── chapter13.png │ │ │ ├── chapter13_01.png │ │ │ ├── chapter13_02.png │ │ │ ├── chapter13_03.png │ │ │ ├── chapter13_04.png │ │ │ ├── chapter13_05.png │ │ │ ├── chapter13_06.png │ │ │ ├── chapter13_07.png │ │ │ ├── chapter13_GA.html │ │ │ ├── chapter13_GA.png │ │ │ ├── chapter13_GA_01.png │ │ │ ├── chapter13_GA_02.png │ │ │ ├── chapter13_GA_03.png │ │ │ ├── chapter13_GA_04.png │ │ │ ├── chapter13_GridSearch.html │ │ │ ├── chapter13_GridSearch.png │ │ │ ├── chapter13_GridSearch_01.png │ │ │ ├── chapter13_GridSearch_02.png │ │ │ ├── chapter13_GridSearch_03.png │ │ │ ├── chapter13_GridSearch_04.png │ │ │ ├── chapter13_GridSearch_05.png │ │ │ ├── chapter13_GridSearch_06.png │ │ │ ├── chapter13_GridSearch_07.png │ │ │ ├── chapter13_PSO.html │ │ │ ├── chapter13_PSO.png │ │ │ ├── chapter13_PSO_01.png │ │ │ ├── chapter13_PSO_02.png │ │ │ ├── chapter13_PSO_03.png │ │ │ └── chapter13_PSO_04.png │ │ ├── 案例14 SVM神经网络的回归预测分析---上证开盘指数预测 │ │ ├── chapter14.m │ │ ├── chapter14_sh.mat │ │ └── html │ │ │ ├── chapter14.html │ │ │ ├── chapter14.png │ │ │ ├── chapter14_01.png │ │ │ ├── chapter14_02.png │ │ │ ├── chapter14_03.png │ │ │ ├── chapter14_04.png │ │ │ ├── chapter14_05.png │ │ │ └── chapter14_06.png │ │ ├── 案例15 SVM神经网络的信息粒化时序回归预测 │ │ ├── FIG_D.m │ │ ├── chapter15.m │ │ ├── chapter15_sh.mat │ │ ├── html │ │ │ ├── chapter15.html │ │ │ ├── chapter15.png │ │ │ ├── chapter15_01.png │ │ │ ├── chapter15_02.png │ │ │ ├── chapter15_03.png │ │ │ ├── chapter15_04.png │ │ │ ├── chapter15_05.png │ │ │ ├── chapter15_06.png │ │ │ ├── chapter15_07.png │ │ │ ├── chapter15_08.png │ │ │ ├── chapter15_09.png │ │ │ ├── chapter15_10.png │ │ │ ├── chapter15_11.png │ │ │ ├── chapter15_12.png │ │ │ ├── chapter15_13.png │ │ │ ├── chapter15_14.png │ │ │ ├── chapter15_15.png │ │ │ ├── chapter15_16.png │ │ │ └── chapter15_17.png │ │ └── original.tif │ │ ├── 案例16 单层竞争神经网络的数据分类—患者癌症发病预测 │ │ ├── chapter16.m │ │ ├── gene.mat │ │ └── gene.txt │ │ ├── 案例17 SOM神经网络的数据分类--柴油机故障诊断 │ │ ├── addon.m │ │ ├── chapter17.m │ │ ├── p.mat │ │ └── 运行说明.txt │ │ ├── 案例18 Elman神经网络的数据预测—电力负荷预测模型研究 │ │ ├── chapter18.m │ │ └── data.mat │ │ ├── 案例19 概率神经网络的分类预测-基于PNN变压器故障诊断 │ │ ├── chapter19.m │ │ └── data.mat │ │ ├── 案例2 BP神经网络的非线性系统建模-非线性函数拟合 │ │ ├── BP.m │ │ ├── BP_Hidden.m │ │ └── data.mat │ │ ├── 案例20 神经网络变量筛选—基于BP的神经网络变量筛选 │ │ └── chapter20.m │ │ ├── 案例21 LVQ神经网络的分类——乳腺肿瘤诊断 │ │ ├── chapter21_bp.m │ │ ├── chapter21_lvq.m │ │ ├── crossvalidation_lvq.m │ │ └── data.mat │ │ ├── 案例22 LVQ神经网络的预测——人脸朝向识别 │ │ ├── Images │ │ │ ├── 10_1.bmp │ │ │ ├── 10_2.bmp │ │ │ ├── 10_3.bmp │ │ │ ├── 10_4.bmp │ │ │ ├── 10_5.bmp │ │ │ ├── 1_1.bmp │ │ │ ├── 1_2.bmp │ │ │ ├── 1_3.bmp │ │ │ ├── 1_4.bmp │ │ │ ├── 1_5.bmp │ │ │ ├── 2_1.bmp │ │ │ ├── 2_2.bmp │ │ │ ├── 2_3.bmp │ │ │ ├── 2_4.bmp │ │ │ ├── 2_5.bmp │ │ │ ├── 3_1.bmp │ │ │ ├── 3_2.bmp │ │ │ ├── 3_3.bmp │ │ │ ├── 3_4.bmp │ │ │ ├── 3_5.bmp │ │ │ ├── 4_1.bmp │ │ │ ├── 4_2.bmp │ │ │ ├── 4_3.bmp │ │ │ ├── 4_4.bmp │ │ │ ├── 4_5.bmp │ │ │ ├── 5_1.bmp │ │ │ ├── 5_2.bmp │ │ │ ├── 5_3.bmp │ │ │ ├── 5_4.bmp │ │ │ ├── 5_5.bmp │ │ │ ├── 6_1.bmp │ │ │ ├── 6_2.bmp │ │ │ ├── 6_3.bmp │ │ │ ├── 6_4.bmp │ │ │ ├── 6_5.bmp │ │ │ ├── 7_1.bmp │ │ │ ├── 7_2.bmp │ │ │ ├── 7_3.bmp │ │ │ ├── 7_4.bmp │ │ │ ├── 7_5.bmp │ │ │ ├── 8_1.bmp │ │ │ ├── 8_2.bmp │ │ │ ├── 8_3.bmp │ │ │ ├── 8_4.bmp │ │ │ ├── 8_5.bmp │ │ │ ├── 9_1.bmp │ │ │ ├── 9_2.bmp │ │ │ ├── 9_3.bmp │ │ │ ├── 9_4.bmp │ │ │ └── 9_5.bmp │ │ ├── chapter22_bp.m │ │ ├── chapter22_lvq.m │ │ ├── chapter_svm.m │ │ ├── crossvalind_lvq.m │ │ ├── feature_extraction.m │ │ ├── lvq1_train.m │ │ ├── lvq2_train.m │ │ ├── lvq_predict.m │ │ └── test.m │ │ ├── 案例23 小波神经网络的时间序列预测-短时交通流量预测 │ │ ├── d_mymorlet.m │ │ ├── mymorlet.m │ │ ├── traffic_flux.mat │ │ ├── wavenn.asv │ │ └── wavenn.m │ │ ├── 案例24 模糊神经网络的预测算法-嘉陵江水质评价 │ │ ├── FuzzyNet.m │ │ ├── data1.mat │ │ └── data2.mat │ │ ├── 案例25 广义神经网络的聚类算法-网络入侵聚类 │ │ ├── FCMGRNN.m │ │ └── netattack.mat │ │ ├── 案例26 粒子群算法的寻优算法-非线性函数极值寻优 │ │ ├── PSO.m │ │ ├── PSOMutation.m │ │ └── fun.m │ │ ├── 案例27 遗传算法的优化计算——建模自变量降维 │ │ ├── data.mat │ │ ├── de_code.m │ │ ├── fitness.m │ │ ├── gabpEval.m │ │ ├── gadecod.m │ │ └── main.m │ │ ├── 案例28 灰色神经网络的预测算法—订单需求预测 │ │ ├── Greynet.m │ │ └── data.mat │ │ ├── 案例29 kohonen网络的聚类算法—网络入侵聚类 │ │ ├── Kohonen.m │ │ ├── SKohonen.m │ │ └── data.mat │ │ ├── 案例3 遗传算法优化BP神经网络-非线性函数拟合 │ │ ├── BP.m │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Decode.m │ │ ├── Genetic.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── data.mat │ │ ├── fun.m │ │ └── test.m │ │ ├── 案例30 神经网络GUI的实现--基于GUI的神经网络拟合 │ │ └── 案例30 神经网络GUI的实现--基于GUI的神经网络拟合.txt │ │ ├── 案例4 神经网络遗传算法函数极值寻优-非线性函数极值 │ │ ├── BP.m │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Genetic.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── data.m │ │ ├── data.mat │ │ ├── fun.m │ │ ├── net.mat │ │ ├── test.asv │ │ └── test.m │ │ ├── 案例5 基于BP_Adaboost的强分类器设计-公司财务预警建模 │ │ ├── Bp_Ada_Fore.m │ │ ├── Bp_Ada_Sort.m │ │ ├── data.mat │ │ └── data1.mat │ │ ├── 案例6 PID神经元网络解耦控制算法_多变量系统控制 │ │ ├── MPID.m │ │ ├── MPIDCS.m │ │ ├── MPIDDLX.m │ │ ├── draw.m │ │ ├── fun.m │ │ └── pso.m │ │ ├── 案例7 RBF网络的回归-非线性函数回归的实现 │ │ ├── chapter7_1.m │ │ ├── chapter7_2.m │ │ └── 运行提示.txt │ │ ├── 案例8 GRNN的数据预测-基于广义回归神经网络货运量预测 │ │ ├── best.mat │ │ ├── chapter8.1.m │ │ ├── chapter8.2.asv │ │ ├── chapter8.2.m │ │ ├── data.mat │ │ └── 运行提示.txt │ │ └── 案例9 离散Hopfield神经网络的联想记忆—数字识别 │ │ ├── chapter9.m │ │ ├── data0.mat │ │ ├── data1.mat │ │ ├── data1_noisy.mat │ │ ├── data2.mat │ │ ├── data2_noisy.mat │ │ ├── data3.mat │ │ ├── data4.mat │ │ ├── data5.mat │ │ ├── data6.mat │ │ ├── data7.mat │ │ ├── data8.mat │ │ ├── data9.mat │ │ └── waiji.m └── 遗传算法 │ ├── TSP(GA) │ ├── Final Solution.pdf │ ├── README.txt │ ├── china.mat │ ├── crossover.m │ ├── distancematrix.m │ ├── fpdfprinter.m │ ├── main.m │ ├── mutation.m │ ├── plotcities.m │ ├── plotroute.m │ ├── select.m │ └── totaldistance.m │ ├── example_1.txt │ ├── genetic_algorithms.m │ ├── 求解函数最小值 │ ├── f.m │ └── find_function_min.m │ └── 遗传算法求解函数优化问题 │ ├── cro.m │ ├── ft.m │ ├── ga.m │ ├── init.mat │ ├── main.m │ ├── mut.m │ ├── n2to10.m │ ├── objf.m │ ├── pro.m │ └── sel.m ├── ImmuneAlgorithm ├── immune_algorithm.m └── immunity.m ├── IntegerProgramming ├── assgin_integer_prog.m ├── example_1.m └── monte_carro.m ├── IntelligenceAlgorithm ├── chapter1 谢菲尔德大学的matlab遗传算法工具箱 │ ├── example1.m │ ├── example2.m │ └── gatbx-example │ │ └── gatbx │ │ ├── f1.m │ │ ├── f2.m │ │ ├── gaplotchange.m │ │ ├── parameterfun.m │ │ ├── poll_example.m │ │ ├── readme.doc │ │ ├── runps.m │ │ ├── shubert.m │ │ ├── target.m │ │ ├── targetalloc.m │ │ ├── yj1.m │ │ ├── yj10.m │ │ ├── yj12.m │ │ ├── yj12_1.m │ │ ├── yj2.m │ │ ├── yj3.m │ │ ├── yj4.m │ │ ├── yj5.m │ │ ├── yj6.m │ │ ├── yj7.m │ │ └── yj8.m ├── chapter10 基于粒子群算法的多目标搜索算法 │ ├── data.mat │ └── main.m ├── chapter11 基于多层编码遗传算法的车间调度算法 │ ├── Find.m │ ├── REINS.M │ ├── RWS.M │ ├── SELECT.M │ ├── aberranceJm.m │ ├── across.m │ ├── cal.m │ ├── calP.m │ ├── caltime.m │ ├── main.m │ ├── plotRec.m │ ├── ranking.M │ ├── scheduleData.mat │ └── selectJm.m ├── chapter12 免疫优化算法在物流配送中心选址中的应用 │ ├── Cross.m │ ├── IAdata.mat │ ├── Mutation.m │ ├── Select.m │ ├── bestselect.m │ ├── centre.fig │ ├── concentration.m │ ├── draw.m │ ├── excellence.m │ ├── figure.fig │ ├── fitness.m │ ├── incorporate.m │ ├── main.m │ ├── popinit.m │ ├── similar.m │ ├── test.m │ └── 免疫优化算法-物流配送中心的选址.zip ├── chapter13 粒子群算法的寻优算法 │ ├── sample1 │ │ ├── MexicoHatnew.m │ │ ├── PSO0.m │ │ ├── PSO1.m │ │ ├── PSO2.m │ │ ├── PSO3.m │ │ ├── PSO4.m │ │ ├── fun.m │ │ ├── main.m │ │ └── wchange.m │ ├── sample2-Rastrgrin │ │ ├── PSO.m │ │ ├── fun.m │ │ ├── pso.fig │ │ ├── pso.mat │ │ ├── rastrigrin.fig │ │ └── rastrigrin.m │ └── sample3-Griewankan │ │ ├── Griewank.fig │ │ ├── Griewank.m │ │ ├── PSO.m │ │ ├── fun.m │ │ ├── pso.fig │ │ └── pso.mat ├── chapter14 基于粒子群算法的PID控制器优化设计 │ ├── cell模式下运行结果 │ │ ├── GA_run.html │ │ ├── GA_run.png │ │ ├── GA_run_01.png │ │ ├── PSO.html │ │ ├── PSO.png │ │ ├── PSO_01.png │ │ └── PSO_02.png │ ├── 源程序 │ │ ├── GA_run.m │ │ ├── PID_Model.mdl │ │ ├── PSO.m │ │ └── PSO_PID.m │ └── 问题解决思路.pdf ├── chapter15 基于混合粒子群算法的TSP搜索算法 │ ├── Oliver30.txt │ ├── bayg29.txt │ ├── burma14.txt │ ├── ch130.txt │ ├── ch150.txt │ ├── dist.m │ ├── eil51.txt │ ├── fitness.m │ ├── gr96.txt │ ├── main.m │ ├── pr226.txt │ ├── pr76.txt │ └── st70.txt ├── chapter16 基于动态粒子群算法的动态环境寻优算法 │ ├── DF1function.m │ ├── fitnessRecord.mat │ ├── main.m │ └── result.mat ├── chapter17 基于PSO工具箱的函数寻优算法 │ ├── PSOt │ │ ├── forcecol.m │ │ ├── forcerow.m │ │ ├── goplotpso.m │ │ ├── linear_dyn.m │ │ ├── normmat.m │ │ ├── pso_Trelea_vectorized.m │ │ └── spiral_dyn.m │ └── testfunctions │ │ ├── DeJong_f2.m │ │ ├── DeJong_f3.m │ │ ├── DeJong_f4.m │ │ ├── Foxhole.m │ │ ├── Griewank.m │ │ ├── NDparabola.m │ │ ├── Rastrigin.m │ │ ├── Rosenbrock.m │ │ ├── ackley.m │ │ ├── alpine.m │ │ ├── f6.m │ │ ├── f6_bubbles_dyn.m │ │ ├── f6_linear_dyn.m │ │ ├── f6_spiral_dyn.m │ │ ├── f6mod.m │ │ ├── forcecol.m │ │ ├── forcerow.m │ │ ├── goplotpso.m │ │ ├── linear_dyn.m │ │ ├── main.m │ │ ├── normmat.m │ │ ├── pso_Trelea_vectorized.m │ │ ├── spiral_dyn.m │ │ ├── test_func.m │ │ └── tripod.m ├── chapter18 基于鱼群算法的函数寻优算法 │ ├── example1 │ │ ├── AF_dist.m │ │ ├── AF_follow.m │ │ ├── AF_foodconsistence.m │ │ ├── AF_init.m │ │ ├── AF_prey.m │ │ ├── AF_swarm.m │ │ ├── dist.m │ │ └── example1.m │ └── example2 │ │ ├── AF_dist.m │ │ ├── AF_follow.m │ │ ├── AF_foodconsistence.m │ │ ├── AF_init.m │ │ ├── AF_prey.m │ │ ├── AF_swarm.m │ │ ├── dist.m │ │ └── example2.m ├── chapter19 基于模拟退火算法的TSP算法 │ ├── CityPosition1.mat │ ├── CityPosition2.mat │ ├── CityPosition3.mat │ ├── Distanse.m │ ├── DrawPath.m │ ├── Metropolis.m │ ├── NewAnswer.m │ ├── OutputPath.m │ ├── PathLength.m │ ├── SA_TSP.m │ └── dsxy2figxy.m ├── chapter2 基于遗传算法和非线性规划的函数寻优算法 │ ├── 案例1 │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Genetic.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── fun.m │ │ ├── nonlinear.m │ │ ├── readme.txt │ │ └── test.m │ ├── 案例1非线性 │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── fun.m │ │ ├── main.m │ │ ├── nonlinear.m │ │ └── test.m │ ├── 案例2 │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Genetic.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── fun.m │ │ └── test.m │ ├── 案例2非线性 │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Genetic.asv │ │ ├── Genetic.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── fun.m │ │ ├── nonlinear.m │ │ └── test.m │ ├── 案例3 │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Genetic.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── fun.m │ │ └── test.m │ └── 案例3非线性 │ │ ├── Code.m │ │ ├── Cross.m │ │ ├── Genetic.m │ │ ├── Mutation.m │ │ ├── Select.m │ │ ├── fun.m │ │ ├── nonlinear.m │ │ └── test.m ├── chapter20 基于遗传模拟退火算法的聚类算法 │ ├── FCMfun.m │ ├── FCMpure.m │ ├── GAFCM.m │ ├── ObjFun.m │ ├── SAGAFcmMain.m │ ├── X.mat │ ├── initFCM.m │ └── iterateFCM.m ├── chapter21 模拟退火算法工具箱及应用 │ ├── my_first_SA.m │ └── my_first_SA_run.m ├── chapter22 蚁群算法的优化计算——旅行商问题（TSP）优化 │ ├── citys_data.mat │ └── main.m ├── chapter23 基于蚁群算法的二维路径规划算法 │ ├── DijkstraPlan.m │ ├── DijstraPlan.m │ ├── barrier.txt │ ├── lines.txt │ ├── main.m │ └── matrix.txt ├── chapter24 基于蚁群算法的三维路径规划算法 │ ├── CacuFit.m │ ├── CacuQfz.m │ ├── HeightData.mat │ ├── czfz.m │ ├── data.m │ ├── data1.m │ ├── main.m │ └── searchpath.m ├── chapter25 有导师学习神经网络的回归拟合——基于近红外光谱的汽油辛烷值预测 │ ├── main.m │ └── spectra_data.mat ├── chapter26 有导师学习神经网络的分类——￥尾花种类识别 │ ├── iris_data.mat │ └── main.m ├── chapter27 无导师学习神经网络的分类——矿井突水水源判别 │ ├── main.m │ └── water_data.mat ├── chapter28 支持向量机的分类——基于乳腺组织电阻抗特性的乳腺癌诊断 │ ├── BreastTissue_data.mat │ └── main.m ├── chapter29 支持向量机的回归拟合——混凝土抗压强度预测 │ ├── concrete_data.mat │ └── main.m ├── chapter3 基于遗传算法的BP神经网络优化算法 │ ├── Bpfun.m │ ├── GABPMain.m │ ├── Objfun.m │ ├── callbackfun.m │ ├── data.mat │ └── readme.txt ├── chapter30 极限学习机的回归拟合及分类——对比实验研究 │ ├── elmpredict.m │ ├── elmtrain.m │ ├── iris_data.mat │ ├── main.m │ └── spectra_data.mat ├── chapter4 基于遗传算法的TSP算法 │ ├── CityPosition1.mat │ ├── CityPosition2.mat │ ├── CityPosition3.mat │ ├── Distanse.m │ ├── DrawPath.m │ ├── Fitness.m │ ├── GA_TSP.m │ ├── InitPop.m │ ├── Mutate.m │ ├── OutputPath.m │ ├── PathLength.m │ ├── Recombin.m │ ├── Reins.m │ ├── Reverse.m │ ├── Select.m │ ├── Sus.m │ ├── dsxy2figxy.m │ ├── readme.txt │ └── test.m ├── chapter5 基于遗传算法的LQR控制器优化设计 │ ├── Active_Suspension_LQR.mdl │ ├── GA_LQR.m │ └── GA_LQR_run.m ├── chapter6 遗传算法工具箱详解及应用 │ ├── GA_demo.m │ └── GA_demo_run.m ├── chapter7 多种群遗传算法的函数优化算法 │ ├── EliteInduvidual.m │ ├── MPGA.m │ ├── ObjectFunction.m │ ├── SGA.m │ ├── danyuan.m │ └── immigrant.m ├── chapter8 基于量子遗传算法的函数寻优算法 │ ├── FitnessFunction.m │ ├── InitPop.m │ ├── Objfunction.m │ ├── Qgate.m │ ├── QuantumMain.m │ ├── bin2decFun.m │ └── collapse.m └── chapter9 基于遗传算法的多目标优化算法 │ ├── my_first_multi.m │ └── my_first_multi_run.m ├── Interpolation ├── interp_1D.m ├── interp_2D.m ├── interp_2D_compare.m └── interp_grid.m ├── LinearProgramming ├── invest_model.m └── solve_lp.m ├── MultivariateAnalysis ├── 主成分分析 │ ├── PCA.m │ └── example_1.txt └── 聚类分析 │ ├── data.txt │ ├── edu.txt │ ├── main.m │ ├── system_cluster.m │ └── var_cluster.m ├── NeuralNetwork ├── af_classify_BP.m └── af_classify_LVQ.m ├── NonLinearProgramming ├── fun1.m ├── fun2.m └── non_linear_prog.m ├── OridinaryDifferentialEquation ├── Non_stff.m ├── Stff.m ├── example_func1.m ├── example_func2.m └── general_solution_by_dsolve.m ├── PartialDifferentialEquation ├── 2D_PDE │ └── main.m └── BasicPDE │ ├── bc.m │ ├── ic.m │ ├── main.m │ └── pdefun.m ├── PartialLeastSquares ├── data.txt ├── pls.m └── readme.txt ├── RegressionAnalysis ├── func.m ├── linear_regression.m ├── muti_indeterminate_2_degree_poly.m ├── one_indeterminate_poly.m ├── stepwise_regression.m └── unlinear_regression.m ├── TimeSeries ├── 指数平滑法 │ ├── 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4*x(2)^2)*x(2)^2; 3 | 4 | % Rastrigin's function 5 | y = 20 + x(1)^2 + x(2)^2 - 10*(cos(2*pi*x(1))+cos(2*pi*x(2))); -------------------------------------------------------------------------------- /IntelligenceAlgorithm/chapter22 蚁群算法的优化计算——旅行商问题（TSP）优化/citys_data.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hliangzhao/Mathematical-Model-Implementation/60823be10132f7bd469018fca7d4ace0cace079b/IntelligenceAlgorithm/chapter22 蚁群算法的优化计算——旅行商问题（TSP）优化/citys_data.mat -------------------------------------------------------------------------------- /IntelligenceAlgorithm/chapter22 蚁群算法的优化计算——旅行商问题（TSP）优化/main.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hliangzhao/Mathematical-Model-Implementation/60823be10132f7bd469018fca7d4ace0cace079b/IntelligenceAlgorithm/chapter22 蚁群算法的优化计算——旅行商问题（TSP）优化/main.m 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