├── AHP.zip ├── CCRqq.m ├── CanShu.m ├── DEA.m ├── Estimate_plot.m ├── GA-master ├── GA.bmp ├── README.md ├── best.m ├── calfitvalue.m ├── calobjvalue.m ├── crossover.m ├── decodebinary.m ├── decodechrom.m ├── initpop.m ├── main.m ├── mutation.m └── selection.m ├── GA ├── my_fitness.m ├── my_ga.m └── test_ga.m ├── GuoRui.m ├── JL.xlsx ├── NSGA-II ├── evaluate_objective.m ├── genetic_operator.m ├── initialize_variables.m ├── non_domination_sort_mod.m ├── nsga_2_optimization.m ├── replace_chromosome.m └── tournament_selection.m ├── README.md ├── data.xlsx ├── gray.m ├── result.xlsx ├── 列车交路方案优化遗传算法程序 ├── OD.xlsx ├── README.md ├── Variation.m ├── crossover.m ├── m_Coding.m ├── m_Fitness.m ├── m_Fx.m ├── m_Incoding.m ├── m_InitPop.m ├── m_Select.m ├── myself.m ├── od.m ├── 区间运行时间.xlsx ├── 参数各代最优值-进化代数.png └── 最优适应度值-进化代数.png ├── 图.jpg ├── 时空 ├── NaSch_3.m ├── leadcarupdate.m ├── randslow.m ├── roadstart.m ├── searchfrontcar.m ├── searchlastcar.m ├── searchleadcar.m └── speedstart.m ├── 李嘉诚 ├── Questionnaire_Random.m ├── RandomBox.m └── Research_random.m ├── 毕设程序 ├── CAtraffic.m ├── PlotGriddata.m ├── carin.m ├── cross.m ├── evaluate_objective.m ├── fun_loc.m ├── genetic_operator.m ├── goforward.m ├── initialize_variables.m ├── left_road.m ├── m_ini.m ├── main.m ├── modify.m ├── non_domination_sort_mod.m ├── nsga_2_optimization.m ├── rand_slow.m ├── replace_chromosome.m ├── right_road.m ├── s_ini.m ├── stright_road.m ├── tournament_selection.m ├── zeroshift.m ├── 函数关系图.xlsx └── 图 │ ├── Pareto_1.png │ ├── Pareto_10.png │ ├── Pareto_11.png │ ├── Pareto_12.png │ ├── Pareto_13.png │ ├── Pareto_14.png │ ├── Pareto_15.png │ ├── Pareto_16.png │ ├── Pareto_17.png │ ├── Pareto_18.png │ ├── Pareto_19.png │ ├── Pareto_2.png │ ├── Pareto_20.png │ ├── Pareto_21.png │ ├── Pareto_22.png │ ├── Pareto_23.png │ ├── Pareto_24.png │ ├── Pareto_25.png │ ├── Pareto_26.png │ ├── Pareto_27.png │ ├── Pareto_28.png │ ├── Pareto_29.png │ ├── Pareto_3.png │ ├── Pareto_30.png │ ├── Pareto_31.png │ ├── Pareto_32.png │ ├── Pareto_33.png │ ├── Pareto_34.png │ ├── Pareto_35.png │ ├── Pareto_36.png │ ├── Pareto_37.png │ ├── Pareto_38.png │ ├── Pareto_39.png │ ├── Pareto_4.png │ ├── Pareto_40.png │ ├── Pareto_41.png │ ├── Pareto_42.png │ ├── Pareto_43.png │ ├── Pareto_44.png │ ├── Pareto_45.png │ ├── Pareto_46.png │ ├── Pareto_47.png │ ├── Pareto_48.png │ ├── Pareto_49.png │ ├── Pareto_5.png │ ├── Pareto_50.png │ ├── Pareto_6.png │ ├── Pareto_7.png │ ├── Pareto_8.png │ ├── Pareto_9.png │ └── final_data.xlsx ├── 王园园 ├── Plot3D.m ├── distance.m └── wyy.m ├── 蒋璐.m ├── 蔡伶俐.m ├── 袁昆仑.m ├── 论文 ├── 叶冬冬-1726062035-基于元胞自动机模拟和遗传算法改进的动态网络分配模型分析.pdf ├── 叶冬冬毕业论文答辩.ppt └── 开题答辩.pptx ├── 贲楷晨 ├── CanShu.m ├── Estimate_plot.m └── data.xlsx └── 邢捷 ├── XinJie.m └── square.m /AHP.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/AHP.zip -------------------------------------------------------------------------------- /CCRqq.m: -------------------------------------------------------------------------------- 1 | clear ;clc; 2 | X=[5 6 6; 3 | 7 9 10; 4 | 33 48 31]; 5 | Y=[0.64 0.85 0.7; 6 | 1.45 1.53 1.9]; 7 | n=size(X',1);m=size(X,1);s=size(Y,1); 8 | epsilon=10^(-10); 9 | f=[zeros(1,n) -epsilon*ones(1,m+s) 1]; 10 | A=zeros(1,m+n+s+1);b=0; 11 | LB=zeros(n+m+1,1);UB=[]; 12 | LB(n+m+s+1)=-Inf; 13 | for i=1:n; 14 | Aeq=[X eye(m) zeros(m,s) -X(:,i) 15 | Y zeros(s,m) -eye(s) zeros(s,1)]; 16 | beq=[zeros(m,1) 17 | Y(:,1)]; 18 | w(:,i)=linprog(f,A,b,Aeq,beq,LB,UB); 19 | end 20 | w 21 | lambda=w(1:n,:) %输出lambuda 22 | s_minus=w(n+1:m+n,:) %输出s+ 23 | s_plus=w(n+m+1:n+m+s,:) %输出s- 24 | theta=w(n+m++s+1,:) %输出sita -------------------------------------------------------------------------------- /CanShu.m: -------------------------------------------------------------------------------- 1 | % 验证灰色预测模型精度，输出各参数值 2 | clear 3 | x = xlsread('data.xlsx'); 4 | for j = 1:size(x,2) 5 | x = xlsread('data.xlsx'); 6 | x0=x(:,j)'; 7 | n=length(x0); 8 | lamda=x0(1:n-1)./x0(2:n) ; 9 | xlswrite('result.xlsx',{'lamda'},j,'A1'); 10 | xlswrite('result.xlsx',lamda(:),j,'A2'); 11 | range=minmax(lamda); 12 | xlswrite('result.xlsx',{'range'},j,'B1'); 13 | xlswrite('result.xlsx',range(:),j,'B2'); 14 | x1=cumsum(x0); 15 | xlswrite('result.xlsx',{'x1'},j,'C1'); 16 | xlswrite('result.xlsx',x1(:),j,'C2'); 17 | for i=2:n 18 | z(i)=0.5*(x1(i)+x1(i-1)); 19 | end 20 | B=[-z(2:n)',ones(n-1,1)]; 21 | Y=x0(2:n)'; 22 | u=B\Y; 23 | xlswrite('result.xlsx',{'u'},j,'D1'); 24 | xlswrite('result.xlsx',u(:),j,'D2'); 25 | x=dsolve('Dx+a*x=b','x(0)=x0'); 26 | x=subs(x,{'a','b','x0'},{u(1),u(2),x1(1)}); 27 | yuce1=double(subs(x,'t',[0:n-1])); 28 | yuce2=double(subs(x,'t',[0:n+1])); 29 | digits(6),y=vpa(x); %为提高预测精度，先计算预测值，再显示微分方程的解 30 | 31 | yuce=[x0(1),diff(yuce1)]; 32 | yuce3= [x0(1),diff(yuce2)]; 33 | xlswrite('result.xlsx',{'yuce'},j,'E1'); 34 | xlswrite('result.xlsx',yuce3(:),j,'E2'); 35 | epsilon=x0-yuce; %计算残差 36 | xlswrite('result.xlsx',{'epsilon'},j,'F1'); 37 | xlswrite('result.xlsx', epsilon(:),j,'F2'); 38 | delta=abs(epsilon./x0); %计算相对误差 39 | xlswrite('result.xlsx',{'delta'},j,'G1'); 40 | xlswrite('result.xlsx', delta(:),j,'G2'); 41 | rho=1-(1-0.5*u(1))/(1+0.5*u(1))*lamda; %计算级比偏差值 42 | xlswrite('result.xlsx',{'rho'},j,'H1'); 43 | xlswrite('result.xlsx', rho(:),j,'H2'); 44 | clear 45 | end -------------------------------------------------------------------------------- /DEA.m: -------------------------------------------------------------------------------- 1 | clc 2 | clear 3 | X=[40.4 23.1 33.6 43.2; 4 | 2.45 8.56 5.67 10.03; 5 | 20 70 93 120; 6 | 2500 2000 2500 4000]; 7 | Y=[2 3 3 2; 8 | 0.76 0.68 0.83 0.89; 9 | 650 850 800 700]; 10 | %%此前为数据的处理，只需按列输入每个程序的X和Y指标集数据即可，无需改动程序。 11 | %% 12 | 13 | A1=[X;Y]; 14 | [m,~]=size(X); 15 | [s,~]=size(Y); 16 | [~,n]=size(A1); 17 | A2=blkdiag(eye(m),-eye(s)); 18 | i=1; 19 | %修改此处的i分别等于1至n，即可获得不同目标的threta值。 20 | A3=[-X(:,i);zeros(s,1)]; 21 | A=[A1 A2 A3]; 22 | B=[zeros(m,1);Y(:,i)]; 23 | lb=zeros(n+m+s+1,1); 24 | x=linprog([zeros(1,n+m+s) 1],A,B,[],[],lb,[]); 25 | theta=x(n+m+s+1) 26 | lambda=x(1:n) 27 | s_=x(n+1:m+n) 28 | s_plus=x(m+n+1:m+n+s-1) 29 | -------------------------------------------------------------------------------- /Estimate_plot.m: -------------------------------------------------------------------------------- 1 | %************************************% 2 | %预测各数据预测值，并绘制增长趋势图 3 | %************************************% 4 | %建立符号变量a(发展系数)和b(灰作用量) 5 | clear 6 | 7 | %原始数列 A 8 | x = xlsread('data.xlsx'); 9 | for j = 1:size(x, 2) 10 | syms a b; 11 | c = [a b]'; 12 | x = xlsread('data.xlsx'); 13 | A = x(:,j)'; 14 | n = length(A); 15 | %对原始数列 A 做累加得到数列 B 16 | B = cumsum(A); 17 | %对数列 B 做紧邻均值生成 18 | for i = 2:n 19 | C(i) = (B(i) + B(i - 1))/2; 20 | end 21 | C(1) = []; 22 | %构造数据矩阵 23 | B = [-C;ones(1,n-1)]; 24 | Y = A; Y(1) = []; Y = Y'; 25 | %使用最小二乘法计算参数 a(发展系数)和b(灰作用量) 26 | c = inv(B*B')*B*Y; 27 | c = c'; 28 | a = c(1); b = c(2); 29 | %预测后续数据 30 | F = []; F(1) = A(1); 31 | for i = 2:(n+10) 32 | F(i) = (A(1)-b/a)/exp(a*(i-1))+ b/a; 33 | end 34 | %对数列 F 累减还原,得到预测出的数据 35 | G = []; G(1) = A(1); 36 | for i = 2:(n+10) 37 | G(i) = F(i) - F(i-1); %得到预测出来的数据 38 | end 39 | disp('预测数据为：'); 40 | G 41 | %绘制曲线图 42 | t1 = 2005:2017; 43 | t2 = 2006:2028; 44 | figure(1); 45 | %set(gcf,'outerposition',get(0,'screensize')); 46 | plot(t1, A,'ro'); 47 | hold on; 48 | plot(t2, G, 'g-'); 49 | xlabel('年份'); 50 | danwei = ['万吨','万吨','万吨','亿元','亿元']; 51 | ylabel(danwei(j)); 52 | set(gca,'looseInset',[0 0 0 0]); 53 | txt1 = ["实际货运量增长","实际工业产量增长","实际新能源增长","实际国内贸易增长","实际消费支出增长"]; 54 | txt2 = ["预测货运量增长","预测工业产量增长","预测新能源增长","预测国内贸易增长","预测消费支出增长"]; 55 | legend(txt1(j),txt2(j)); 56 | set(gca,'FontSize',12,'Fontname', 'Times New Roman'); 57 | set(get(gca,'XLabel'),'Fontsize',12,'Fontname', '宋体'); 58 | set(get(gca,'YLabel'),'Fontsize',12,'Fontname', '宋体'); 59 | set(get(gca,'legend'),'Fontsize',12,'Fontname', '宋体'); 60 | set(get(gca,'title'),'Fontsize',12,'Fontname', '宋体'); 61 | print(gcf,'-dpng','-r300',['图',num2str(j)]); 62 | close; 63 | clear 64 | end -------------------------------------------------------------------------------- /GA-master/GA.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/GA-master/GA.bmp -------------------------------------------------------------------------------- /GA-master/README.md: -------------------------------------------------------------------------------- 1 | # GA 2 | 基础遗传算法求二次函数最大值 3 | matlab2016b 4 | -------------------------------------------------------------------------------- /GA-master/best.m: -------------------------------------------------------------------------------- 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[元胞自动机-遗传算法😂](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/毕设程序) 9 | - [NSGA—II👾](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/NSGA-II) 10 | - [灰色预测-交通运输量自动生成🙃🙃](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/贲楷晨) 11 | - [SPSS处理调查问卷前的调查问卷预生成🤦‍♀️🤦‍♂️](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/李嘉诚) 12 | - [欧几里得坐标系计算小区距离🧔](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/王园园) 13 | - [莫名其妙的随机数😰😰😰](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/邢捷) 14 | - [模糊综合评价法😘](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/蔡伶俐.m) 15 | - [没有运动手环数据接口所以只能手动生成的'手环数据生成器'😨😨😨](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/蒋璐.m) 16 | - [AHP](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/袁昆仑.m) 17 | - [CCR](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/CCRqq.m) 18 | - [DEA](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/DEA.m) 19 | - [灰色预测](https://github.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/blob/master/gray.m) 20 | 21 | ## 待更 22 | 23 | ## 致谢有亮点 -------------------------------------------------------------------------------- /data.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/data.xlsx -------------------------------------------------------------------------------- /gray.m: -------------------------------------------------------------------------------- 1 | % 验证GDP灰色预测模型精度，输出各参数值 2 | clear 3 | x0=[5202,5031,4899,4733,4679,4668,4642,4601]; 4 | n=length(x0); 5 | lamda=x0(1:n-1)./x0(2:n) 6 | range=minmax(lamda) 7 | x1=cumsum(x0) 8 | for i=2:n 9 | z(i)=0.5*(x1(i)+x1(i-1)); 10 | end 11 | B=[-z(2:n)',ones(n-1,1)]; 12 | Y=x0(2:n)'; 13 | u=B\Y 14 | x=dsolve('Dx+a*x=b','x(0)=x0'); 15 | x=subs(x,{'a','b','x0'},{u(1),u(2),x1(1)}); 16 | yuce1=double(subs(x,'t',[0:n-1])); 17 | yuce2=double(subs(x,'t',[0:n+1])); 18 | digits(6),y=vpa(x) %为提高预测精度，先计算预测值，再显示微分方程的解 19 | %yuce=[x0(1),diff(yuce1)] 20 | yuce3=[x0(1),diff(yuce2)] 21 | epsilon=x0-yuce %计算残差 22 | delta=abs(epsilon./x0) %计算相对误差 23 | rho=1-(1-0.5*u(1))/(1+0.5*u(1))*lamda %计算级比偏差值 -------------------------------------------------------------------------------- /result.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/result.xlsx -------------------------------------------------------------------------------- /列车交路方案优化遗传算法程序/OD.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/列车交路方案优化遗传算法程序/OD.xlsx -------------------------------------------------------------------------------- /列车交路方案优化遗传算法程序/README.md: -------------------------------------------------------------------------------- 1 | 2 | # 1️⃣ 问题陈述 3 | ## 1️⃣.1️⃣ 需要解决的问题 4 |   目前地铁一般采用如下的**单一交路** 5 | ： 6 | ![在这里插入图片描述](https://img-blog.csdnimg.cn/2020051300111441.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0V4Y2FsaWJ1clVsaW1pdGVk,size_16,color_FFFFFF,t_70#pic_center)目前，我国绝大多数城市都采用这种交路形式，但是当断面客流量分布不均匀时容易造成线路运能浪费，客流拥挤。 7 | 8 | 替代方案就是用**大小交路**： 9 | 10 | ![在这里插入图片描述](https://img-blog.csdnimg.cn/20200513001301994.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0V4Y2FsaWJ1clVsaW1pdGVk,size_16,color_FFFFFF,t_70#pic_center) 11 | ![在这里插入图片描述](https://img-blog.csdnimg.cn/20200513001308221.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0V4Y2FsaWJ1clVsaW1pdGVk,size_16,color_FFFFFF,t_70#pic_center) 12 | 使用遗传算法程序就是在既定的OD矩阵下找到最优的大小交路的往返站$S_a,S_b$以及相应的大小交路的发车频率$f_1,f_2$，也就是在遗传算法每次运行中，根据不同的大小交路折返站的设置，划分预定的OD出行矩阵，然后计算目标函数，判断是否达到最优。 13 | 14 | ## 1️⃣.2️⃣ 变量定义 15 | - $Q_1$——出行$O$点或$D$点位于小交路覆盖区段外出行以及$OD$均位于小交路覆盖区段外的客流量，人； 16 | - $Q_2$——出行$OD$位于小交路覆盖区段的客流量，人； 17 | - $t_{1d}、t_{2d}$——$Q_1、Q_2$对应乘客的平均候车时间，$s$; 18 | - $q_{od}$——在车站$o$上车，在车站$d$下车的客流量，人； 19 | - $i$——列车交路的集合，$i=\{1,2\}$，$1$代表大交路，$2$代表小交路； 20 | - $f_i$——大小交路运行方式下的交路i的发车频率，对/小时； 21 | - $f$——单一交路运行方式下的发车频率，对/小时； 22 | - $f_{min}$——最小发车频率，设置为$12$对/小时； 23 | - $T_{1周}、T_{2周}$——大小交路列车周转时间，$s$; 24 | - $t_{运,j}$——列车在区间$j$的纯运行时间，$s$； 25 | - $t_{停,j}$——列车在车站$h$的停站时间，$s$，设置为$30s$； 26 | - $t_{折}$——列车在终点站、中间站的最小折返间隔时间，$s$，设置为$120s$; 27 | - $C_z$——列车定员，即标准载客人数，人，设置为$1460$； 28 | - $\alpha$——列车满载率上限，设置为100%； 29 | - $I_o$——列车最小追踪间隔，$s$，设置为$120s$; 30 | ## 1️⃣.3️⃣ 目标函数 31 | $$\min _{Z}=Q_{1} \cdot t_{1 d}+Q_{2} \cdot t_{2 d}$$ 32 | 其中 33 | $$Q_{1}=\sum_{d=1}^{n} \sum_{o=1}^{n} q_{o d}-Q_{2}\\Q_{2=} \sum_{d=o+1}^{b} \sum_{o=a}^{b-1} q_{o, d}+\sum_{d=a}^{o-1} \sum_{o=a+1}^{b} q_{o, d}$$ 34 | $$t_{2 d}=\frac{1}{2} \cdot \frac{60}{f_{1}+f_{2}}\\ \\t_{1 d}=\frac{1}{2} \cdot \frac{60}{f}$$ 35 | 36 | ## 1️⃣.4️⃣ 约束条件 37 | - 列车数量 38 | $$\left[\frac{T_{\text {周1}}}{60} \cdot f_{1}\right]+\left[\frac{T_{\text {周} 2}}{60} \cdot f_{2}\right] \leq\left[\frac{T_{\text {周} 1}}{60} \cdot f\right]$$ 39 | 其中 40 | $$T_{\text {周} 1}=2 \cdot\left(\sum_{j=1}^{n-1} t_{\text {运}},_{j}+\sum_{h=1}^{n} t_{\text {停, } h}+\sum t_{\text {折}}\right)\\T_{\text {周} 2}=2 \cdot\left(\sum_{j=a}^{b-1} t_{\text {运}},_{j}+\sum_{h=a}^{b} t_{\text {停, } h}+\sum t^{'}_{\text {折}}\right)$$ 41 | 42 | - 满载率约束 43 | $$\max _{j}\left(\sum_{d=j+1}^{n} \sum_{o=1}^{j} q_{o d} / \sum_{i=1}^{2} f_{i} \cdot \sum_{d=1}^{j} \sum_{o=j+1}^{n} q_{o d} / \sum_{i=1}^{2} f_{i}\right) \leq \alpha \cdot C_z$$ 44 | 45 | - 满足最小追踪间隔 46 | $$f_{1}+f_{2} \leq \frac{3600}{I_{0}}$$ 47 | 48 | - 折返站折返能力 49 | $$f_1\le\frac{3600}{t_{折}}\\f_2\le\frac{3600}{t_折}$$ 50 | 51 | - 满足最小发车频率 52 | $$f_1>=f_{min}$$ 53 | 54 | - 其他约束 55 | $$f_i\in N\\ 1\le a 35000 ; 153 | if sum(sum(N)) < 40000; 154 | xlswrite('test.xlsx',N,'Sheet1') 155 | end 156 | end 157 | ``` 158 | 159 | ## 2️⃣.3️⃣ `m_InitPop.m`——初始化种群 160 | 161 | ```python 162 | function pop=m_InitPop(numpop,irange_l,irange_r) 163 | %% 初始化种群 164 | % 输入：numpop--种群大小； 165 | % [irange_l,irange_r]--初始种群所在的区间 166 | pop=[]; 167 | for j = 1:numpop 168 | for i=1:4 169 | % 因为a,b,f1,f2要求整数，所以生成随机整数 170 | pop(i,j)= round(irange_l+(irange_r-irange_l)*rand); 171 | end 172 | end 173 | 174 | ``` 175 | ## 2️⃣.4️⃣ `m_Select.m`——选择 176 | 177 | ```python 178 | function parentPop=m_Select(matrixFitness,pop,SELECTRATE) 179 | %% 选择 180 | % 输入：matrixFitness--适应度矩阵 181 | % pop--初始种群 182 | % SELECTRATE--选择率 183 | 184 | sumFitness=sum(matrixFitness(:));%计算所有种群的适应度 185 | 186 | accP=cumsum(matrixFitness/sumFitness);%累积概率 187 | %轮盘赌选择算法 188 | for n=1:round(SELECTRATE*size(pop,2)) 189 | matrix=find(accP>rand); %找到比随机数大的累积概率 190 | if isempty(matrix) 191 | continue 192 | end 193 | parentPop(:,n)=pop(:,matrix(1));%将首个比随机数大的累积概率的位置的个体遗传下去 194 | end 195 | end 196 | ``` 197 | ## 2️⃣.5️⃣ `Crossover.m`——交叉 198 | 199 | ```python 200 | %% 子函数 201 | % 202 | %题 目：Crossover 203 | % 204 | %% 205 | %输 入： 206 | % parentsPop 上一代种群 207 | % NUMPOP 种群大小 208 | % CROSSOVERRATE 交叉率 209 | %输 出： 210 | % kidsPop 下一代种群 211 | % 212 | %% 213 | function kidsPop = Crossover(parentsPop,NUMPOP,CROSSOVERRATE) 214 | kidsPop = {[]};n = 1; 215 | while size(kidsPop,2) 35 || b > 35 || f1 >35 || f2 >35 313 | fitness(n) = 1/1000000000; 314 | continue; 315 | end 316 | %% 3) 列车数量约束 317 | if (sum(h) * 120 + 1170) *( f1 - 16) + (sum(h(a: b-1)) + (b - a + 1) * 30 + 120) * f2 > 0 318 | fitness(n) = 1/1000000000; 319 | continue; 320 | end 321 | %% 4) 满载率约束 322 | % constraint2 = []; 323 | % for j = 2:33 324 | % constraint2(j) = (sum(sum(OD(1:j, j+1:35)))/(f1+f2)) * (sum(sum(OD(j+1:35,1:j)))/(f1+f2)); 325 | % end 326 | % if max(constraint2) > 1 * 1460 327 | % fitness(n) = 1/1000000000; 328 | % continue; 329 | % end 330 | %% 5) 最小追踪间隔 331 | if f1 + f2 > 30 332 | fitness(n) = 1/1000000000; 333 | continue; 334 | end 335 | %% 5) 最小发车间隔 336 | if f1 < 12 337 | fitness(n) = 1/1000000000; 338 | continue; 339 | end 340 | %% 主要适应度函数，设置为目标函数的倒数，即目标函数要求最小，那么越小，适应度就越大 341 | fitness(n)= 1/m_Fx(pop(:,n), OD); 342 | end 343 | 344 | ``` 345 | 346 | ## 2️⃣.🔟 `m_Fx.m`——目标函数(重要) 347 | 348 | ```python 349 | function y=m_Fx(x, OD) 350 | %% 要求解的函数 351 | %% Z = Q1 * t1d + Q2 * t2d 352 | y = (sum(sum(OD)) - sum(sum(OD(x(1):x(2),x(1):x(2))))) * (30/x(3)) + sum(sum(OD(x(1):x(2),x(1):x(2)))) * (30/(x(3)+x(4))); 353 | end 354 | ``` 355 | 356 | 357 | # 3️⃣ 运行结果 358 | 359 | ```python 360 | 最优解：71335.4762分钟 361 | 最优解对应的各参数：4,32,14,4 362 | 最大适应度：1.4018e-05 363 | ``` 364 | 365 | 即设置第$4$和第$32$个站点为大小交路折返站，大交路发车频率为$14$列/小时，小交路发车频率为$4$列/小时，最低平均等待时间为$71335$分钟。 366 | 367 | 图像结果： 368 | ![在这里插入图片描述](https://img-blog.csdnimg.cn/20200513011016570.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0V4Y2FsaWJ1clVsaW1pdGVk,size_16,color_FFFFFF,t_70#pic_center) 369 | ![在这里插入图片描述](https://img-blog.csdnimg.cn/20200513011005955.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0V4Y2FsaWJ1clVsaW1pdGVk,size_16,color_FFFFFF,t_70#pic_center) 370 | -------------------------------------------------------------------------------- /列车交路方案优化遗传算法程序/Variation.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/列车交路方案优化遗传算法程序/Variation.m -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | Mu = 26; 2 | sigma = 10; 3 | N = round(normrnd(Mu, sigma, [35 35])); 4 | N = N + abs(min(N)); 5 | sum(sum(N)) 6 | if sum(sum(N)) > 35000 ; 7 | if sum(sum(N)) < 40000; 8 | xlswrite('test.xlsx',N,'Sheet1') 9 | end 10 | end -------------------------------------------------------------------------------- /列车交路方案优化遗传算法程序/区间运行时间.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/列车交路方案优化遗传算法程序/区间运行时间.xlsx -------------------------------------------------------------------------------- /列车交路方案优化遗传算法程序/参数各代最优值-进化代数.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/列车交路方案优化遗传算法程序/参数各代最优值-进化代数.png 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-------------------------------------------------------------------------------- /时空/searchfrontcar.m: -------------------------------------------------------------------------------- 1 | function [location_frontcar]=searchfrontcar(current_location,matrix_cells) 2 | i=length(matrix_cells); 3 | if current_location==i 4 | location_frontcar=0; 5 | else 6 | for j=current_location+1:i 7 | if matrix_cells(j)~=0 8 | location_frontcar=j; 9 | break; 10 | else 11 | location_frontcar=0; 12 | end 13 | end 14 | end -------------------------------------------------------------------------------- /时空/searchlastcar.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/时空/searchlastcar.m -------------------------------------------------------------------------------- /时空/searchleadcar.m: -------------------------------------------------------------------------------- 1 | function [location_leadcar]=searchleadcar(matrix_cells) 2 | i=length(matrix_cells); 3 | for j=1:i 4 | if matrix_cells(i-j+1)~=0 5 | location_leadcar=i-j+1; 6 | break; 7 | else 8 | location_leadcar=0; 9 | end 10 | end -------------------------------------------------------------------------------- /时空/speedstart.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/时空/speedstart.m -------------------------------------------------------------------------------- /李嘉诚/Questionnaire_Random.m: -------------------------------------------------------------------------------- 1 | %调查问卷随机数据 2 | 3 | Q1 = [179 348 98]; 4 | Q2 = [333 292]; 5 | Q3 = [9 188 278 100 35 15]; 6 | Q4 = [348 277]; 7 | Q5 = [113 129 187 100 75 21]; 8 | 9 | Q1_ans = RandomBox(3,Q1); 10 | Q2_ans = RandomBox(2,Q2); 11 | Q3_ans = RandomBox(6,Q3); 12 | Q4_ans = RandomBox(2,Q4); 13 | Q5_ans = RandomBox(6,Q5); 14 | 15 | number = 1:625; 16 | data = [Q1_ans,Q2_ans,Q3_ans,Q4_ans,Q5_ans]; 17 | group_cell = horzcat(number',data); 18 | xlswrite('数据.xlsx',group_cell,1); -------------------------------------------------------------------------------- /李嘉诚/RandomBox.m: -------------------------------------------------------------------------------- 1 | function BOX = RandomBox(num,num_matrix) 2 | Box = zeros(625,1); 3 | if num == 2 4 | Box(1:num_matrix(1),:) = 1; 5 | Box(num_matrix(1)+1:end,:) = 2; 6 | end 7 | if num == 3 8 | Box(1:num_matrix(1),:) = 1; 9 | Box(num_matrix(1)+1:num_matrix(1)+num_matrix(2),:) = 2; 10 | Box(num_matrix(1)+num_matrix(2)+1:end,:) = 3; 11 | end 12 | if num == 6 13 | Box(1:num_matrix(1),:) = 1; 14 | Box(num_matrix(1)+1:num_matrix(1)+num_matrix(2),:) = 2; 15 | Box(num_matrix(1)+num_matrix(2)+1:num_matrix(1)+num_matrix(2)+num_matrix(3),:) = 3; 16 | Box(num_matrix(1)+num_matrix(2)+num_matrix(3)+1:num_matrix(1)+num_matrix(2)+num_matrix(3)+num_matrix(4),:) = 4; 17 | Box(num_matrix(1)+num_matrix(2)+num_matrix(3)+num_matrix(4)+1:num_matrix(1)+num_matrix(2)+num_matrix(3)+num_matrix(4)+num_matrix(5),:) = 5; 18 | Box(num_matrix(1)+num_matrix(2)+num_matrix(3)+num_matrix(4)+num_matrix(5)+1:end,:)=6; 19 | end 20 | randindex = randperm(length(Box)); 21 | BOX =Box(randindex); 22 | end 23 | 24 | -------------------------------------------------------------------------------- /李嘉诚/Research_random.m: -------------------------------------------------------------------------------- 1 | %产生调查问卷的随机数据，保存在'数据.xlsx'中的MATLANB代码 2 | train_cost = [103,105,107]; 3 | train_time = [120,150,160]; 4 | train_interval = [60,120,150]; 5 | 6 | coach_cost = [90,100,140]; 7 | coach_time = [180,195,210]; 8 | coach_interval = [30,45,60]; 9 | 10 | car_cost = [140,165]; 11 | car_time = [240,180]; 12 | 13 | group_cell = []; 14 | for i = 1:27 15 | for j = 1:8 16 | switch j 17 | case 1 18 | group_cell(i,j) = train_cost(randi(length(train_cost),1,1)); 19 | case 2 20 | group_cell(i,j) = train_time(randi(length(train_time),1,1)); 21 | case 3 22 | group_cell(i,j)= train_interval(randi(length(train_interval),1,1)); 23 | case 4 24 | group_cell(i,j) = coach_cost(randi(length(coach_cost),1,1)); 25 | case 5 26 | group_cell(i,j) = coach_time(randi(length(coach_time),1,1)); 27 | case 6 28 | group_cell(i,j) = coach_interval(randi(length(coach_interval),1,1)); 29 | case 7 30 | group_cell(i,j) = car_cost(randi(length(car_cost),1,1)); 31 | case 8 32 | group_cell(i,j) = car_time(randi(length(car_time),1,1)); 33 | end 34 | end 35 | end 36 | number = 1:27; 37 | group_cell = horzcat(number',group_cell); 38 | xlswrite('数据.xlsx',group_cell,1); 39 | 40 | 41 | -------------------------------------------------------------------------------- /毕设程序/CAtraffic.m: -------------------------------------------------------------------------------- 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function zukang_left = left_road(n,flag) 2 | global e_1; 3 | x_i1 = e_1 /100 / 72; 4 | if flag == 1 5 | zukang_left = 20 * (1 + 0.15 * (n / e_1) ^ 0.4)... 6 | + (10 * (1 - 0.5 ) ^ 2) / (2 * (1 - 0.5 * x_i1))... 7 | + (x_i1 ^ 2) / (2 * n * (1 - x_i1))... 8 | - 0.65 * ((10 / (n ^ 2)) ^ (1/3)) * (x_i1 ^ (2 + 5 * x_i1)); 9 | else 10 | zukang_left = (10 * (1 - 0.5 ) ^ 2) / (2 * (1 - 0.5 * x_i1))... 11 | + (x_i1 ^ 2) / (2 * n * (1 - x_i1))... 12 | - 0.65 * ((10 / (n ^ 2)) ^ (1/3)) * (x_i1 ^ (2 + 5 * x_i1)); 13 | end 14 | end 15 | 16 | -------------------------------------------------------------------------------- /毕设程序/m_ini.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/毕设程序/m_ini.m -------------------------------------------------------------------------------- /毕设程序/main.m: 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^ (1/3)) * (x_i3 ^ (2 + 5 * x_i3)); 13 | end 14 | end 15 | 16 | -------------------------------------------------------------------------------- /毕设程序/s_ini.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/毕设程序/s_ini.m -------------------------------------------------------------------------------- /毕设程序/stright_road.m: -------------------------------------------------------------------------------- 1 | function zukang_stright = stright_road(n,flag) 2 | global e_2; 3 | x_i2 = e_2 /100 / 92; 4 | if flag == 1 5 | zukang_stright = 30 * (1 + 0.15 * (n / e_2) ^ 0.4)... 6 | + (10 * (1 - 0.5 ) ^ 2) / (2 * (1 - 0.5 * x_i2)) ... 7 | + (x_i2 ^ 2) / (2 * n * (1 - x_i2))... 8 | - 0.65 * ((10 / (n ^ 2)) ^ (1/3)) * (x_i2 ^ (2 + 5 * x_i2)); 9 | else 10 | zukang_stright = (10 * (1 - 0.5 ) ^ 2) / (2 * (1 - 0.5 * x_i2)) ... 11 | + (x_i2 ^ 2) / (2 * n * (1 - x_i2))... 12 | - 0.65 * ((10 / (n ^ 2)) ^ (1/3)) * (x_i2 ^ (2 + 5 * x_i2)); 13 | end 14 | end 15 | 16 | -------------------------------------------------------------------------------- /毕设程序/tournament_selection.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ExcaliburEX/Graduation-Design-and-MATLAB-Code-/3e5b4408bef1abb60edcffd90cec338601375031/毕设程序/tournament_selection.m -------------------------------------------------------------------------------- /毕设程序/zeroshift.m: -------------------------------------------------------------------------------- 1 | function X = zeroshift(X,k) 2 | [m,n]=size(X); 3 | X = circshift(X,k); 4 | if k(1)>0 5 | X(1:k(1),:)=0; 6 | elseif k(1)<0 7 | X(m+k(1)+1:m,:)=0; 8 | end 9 | if k(2)>0 10 | X(:,1:k(2))=0; 11 | elseif k(2)<0 12 | X(:,n+k(2)+1:n)=0; 13 | end 14 | end 15 | 16 | -------------------------------------------------------------------------------- /毕设程序/函数关系图.xlsx: 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set(get(gca,'XLabel'),'Fontsize',20,'Fontname', '宋体'); 20 | set(get(gca,'YLabel'),'Fontsize',20,'Fontname', '宋体'); 21 | set(get(gca,'ZLabel'),'Fontsize',20,'Fontname', '宋体'); 22 | colorbar; 23 | colormap(hsv); 24 | print(1,'-dpng','-r300','选址三维图'); -------------------------------------------------------------------------------- /王园园/distance.m: -------------------------------------------------------------------------------- 1 | function d = distance(x,y,x0,y0) 2 | d = sqrt((x0-x)^2+(y0-y)^2); 3 | 4 | 5 | -------------------------------------------------------------------------------- /王园园/wyy.m: -------------------------------------------------------------------------------- 1 | clear 2 | %读取数据 3 | data = xlsread('data.xlsx','sheet1'); 4 | D = data(:,1); 5 | w = data(:,2); 6 | a = data(:,3); 7 | x = data(:,4); 8 | y = data(:,5); 9 | 10 | %开始计算 11 | x_d = zeros(1,1000000); 12 | y_d = zeros(1,1000000); 13 | awx = zeros(1,1000000); 14 | awy = zeros(1,1000000); 15 | aw = zeros(1,1000000); 16 | d = zeros(1,1000000); 17 | W = zeros(1,1000000); 18 | 19 | for j = 1:length(D) 20 | awx(1) = awx(1) + a(j) * w(j) * x(j); 21 | awy(1) = awy(1) + a(j) * w(j) * y(j); 22 | aw(1) = aw(1) + a(j) * w(j); 23 | end 24 | x_d(1) = awx(1) / aw(1); 25 | y_d(1) = awy(1) / aw(1); 26 | for j = 1:length(D) 27 | d(j)= distance(x_d(1),y_d(1),x(j),y(j)); 28 | end 29 | for j = 1:length(D) 30 | W(1) = W(1)+a(j)* w(j) * d(j); 31 | end 32 | 33 | 34 | for i = 2:1000000 35 | for j = 1:length(D) 36 | awx(i) = awx(i) + a(j) * w(j) * x(j)/d(j); 37 | awy(i) = awy(i) + a(j) * w(j) * y(j)/d(j); 38 | aw(i) = aw(i) + a(j) * w(j)/d(j); 39 | end 40 | x_d(i) = awx(i) / aw(i); 41 | y_d(i) = awy(i) / aw(i); 42 | for j = 1:length(D) 43 | d(j)= distance(x_d(i),y_d(i),x(j),y(j)); 44 | end 45 | for j = 1:length(D) 46 | W(i) = W(i) + a(j)* w(j) * d(j); 47 | end 48 | if W(i)> W(i-1) 49 | disp(['本次结果比上一次大，故最优结果为：',num2str(W(i-1))]); 50 | break 51 | end 52 | if abs(W(i)-W(i-1)) < 0.00000000000000001 53 | disp(['设置精度为0.00000000001时，最优结果为：',num2str(W(i))]); 54 | break 55 | end 56 | disp(['第',num2str(i-1),'次迭代']); 57 | end 58 | x_d(x_d == 0)= []; 59 | y_d(y_d == 0)= []; 60 | W(W==0)=[]; 61 | xlswrite('result.xlsx',{'xd'},1,'A1'); 62 | xlswrite('result.xlsx',x_d(:),1,'A2'); 63 | 64 | xlswrite('result.xlsx',{'yd'},1,'B1'); 65 | xlswrite('result.xlsx',y_d(:),1,'B2'); 66 | 67 | xlswrite('result.xlsx',{'D'},1,'C1'); 68 | xlswrite('result.xlsx',W(:),1,'C2'); 69 | Plot3D(x_d,y_d,W); 70 | 71 | 72 | 73 | 74 | 75 | -------------------------------------------------------------------------------- /蒋璐.m: -------------------------------------------------------------------------------- 1 | clear 2 | N = 720; 3 | HeartBeat = randsrc(N, 1,[1 2 3 4;0.005, 0.4,0.59,0.005]); 4 | HeartData = zeros(N,1); 5 | for i =1:720 6 | switch HeartBeat(i) 7 | case 1 8 | HeartData(i) = 50 + rand()* 10; 9 | case 2 10 | HeartData(i) = 60 + rand()* 20; 11 | case 3 12 | HeartData(i) = 80 + rand()* 20; 13 | case 4 14 | HeartData(i) = 100 + rand()* 20; 15 | end 16 | end 17 | 18 | HighPress = randsrc(N, 1,[1 2 3 4;0.005, 0.4,0.59,0.005]); 19 | HighPressData = zeros(N,1); 20 | for i =1:720 21 | switch HighPress(i) 22 | case 1 23 | HighPressData(i) = 70 + rand()* 20; 24 | case 2 25 | HighPressData(i) = 90 + rand()* 30; 26 | case 3 27 | HighPressData(i) = 120 + rand()* 20; 28 | case 4 29 | HighPressData(i) = 140 + rand()* 20; 30 | end 31 | end 32 | 33 | LowPress = randsrc(N, 1,[1 2 3 4;0.005, 0.4,0.59,0.005]); 34 | LowPressData = zeros(N,1); 35 | for i =1:720 36 | switch LowPress(i) 37 | case 1 38 | LowPressData(i) = 50 + rand()* 10; 39 | case 2 40 | LowPressData(i) = 60 + rand()* 20; 41 | case 3 42 | LowPressData(i) = 80 + rand()* 10; 43 | case 4 44 | LowPressData(i) = 90 + rand()* 20; 45 | end 46 | end 47 | 48 | figure(1); 49 | set(gcf,'outerposition',get(0,'screensize')); 50 | plot(HeartData(:),'b*-'); 51 | set(gca,'FontSize',24,'Fontname', 'Times New Roman'); 52 | xlabel('\fontname{宋体}时间/小时'); 53 | ylabel('\fontname{宋体}心率值'); 54 | %set(gca,'looseInset',[0 0 0 0]); 55 | set(get(gca,'XLabel'),'Fontsize',20,'Fontname', '宋体'); 56 | set(get(gca,'YLabel'),'Fontsize',20,'Fontname', '宋体'); 57 | print(1,'-dpng','-r300','心率图'); 58 | 59 | figure(2); 60 | set(gcf,'outerposition',get(0,'screensize')); 61 | plot(HighPressData,'ro-');hold on; 62 | plot(LowPressData,'gd-'); 63 | set(gca,'FontSize',24,'Fontname', 'Times New Roman'); 64 | xlabel('\fontname{宋体}时间/小时'); 65 | ylabel('\fontname{宋体}血压值'); 66 | %set(gca,'looseInset',[0 0 0 0]); 67 | set(get(gca,'XLabel'),'Fontsize',20,'Fontname', '宋体'); 68 | set(get(gca,'YLabel'),'Fontsize',20,'Fontname', '宋体'); 69 | legend('\fontname{宋体}高血压','\fontname{宋体}低血压'); 70 | print(2,'-dpng','-r300','血压图'); 71 | 72 | xlswrite('data.xlsx',{'心率'},1,'A1'); 73 | xlswrite('data.xlsx',HeartData,1,'A2'); 74 | 75 | xlswrite('data.xlsx',{'高血压'},1,'B1'); 76 | xlswrite('data.xlsx',HighPressData,1,'B2'); 77 | 78 | xlswrite('data.xlsx',{'低血压'},1,'C1'); 79 | xlswrite('data.xlsx',LowPressData,1,'C2'); 80 | 81 | 82 | 83 | 84 | 85 | 86 | -------------------------------------------------------------------------------- /蔡伶俐.m: -------------------------------------------------------------------------------- 1 | clear 2 | clc 3 | %计算模糊综合评价模型的W，R，B，结果放在'result.xlsx' 4 | w1 = [0.4773,0.2880,0.1538,0.0809]; 5 | w2 = [0.149,0.420,0.099,0.264,0.068]; 6 | w3 = [0.238,0.626,0.136]; 7 | w4 = [0.4549,0.1606,0.0619,0.3226 ]; 8 | w5 = [0.538,0.087,0.258,0.117]; 9 | 10 | R1 = [0.2 0.2 0.5 0.1; 11 | 0.1 0.4 0.3 0.2; 12 | 0.6 0.2 0.2 0; 13 | 0.5 0.2 0.2 0.1]; 14 | 15 | R2 = [0.3 0.4 0.2 0.1; 16 | 0.3 0.3 0.3 0.1; 17 | 0.1 0.4 0.3 0.2; 18 | 0.2 0.3 0.3 0.2; 19 | 0.4 0.4 0.2 0]; 20 | 21 | R3 = [0.4 0.3 0.3 0; 22 | 0.5 0.3 0.2 0; 23 | 0.8 0.2 0 0]; 24 | 25 | R4 = [0.6 0.2 0.2 0; 26 | 0.2 0.3 0.5 0; 27 | 0.4 0.4 0.1 0.1; 28 | 0.3 0.2 0 0.5]; 29 | 30 | R5 = [0.7 0.2 0.1 0; 31 | 0.5 0.2 0.2 0.1; 32 | 0.6 0.2 0.2 0.1; 33 | 0.3 0.2 0.2 0.3]; 34 | 35 | B1 = w1 * R1; 36 | B2 = w2 * R2; 37 | B3 = w3 * R3; 38 | B4 = w4 * R4; 39 | B5 = w5 * R5; 40 | 41 | R = [B1;B2;B3;B4;B5]; 42 | A = [0.405,0.253,0.079,0.207,0.056]; 43 | 44 | B = A * R; 45 | 46 | xlswrite('result.xlsx',{'B1'},1,'A1'); 47 | xlswrite('result.xlsx',B1,1,'B1'); 48 | 49 | xlswrite('result.xlsx',{'B2'},1,'A2'); 50 | xlswrite('result.xlsx',B2,1,'B2'); 51 | 52 | xlswrite('result.xlsx',{'B3'},1,'A3'); 53 | xlswrite('result.xlsx',B3,1,'B3'); 54 | 55 | xlswrite('result.xlsx',{'B4'},1,'A4'); 56 | xlswrite('result.xlsx',B4,1,'B4'); 57 | 58 | xlswrite('result.xlsx',{'B5'},1,'A5'); 59 | xlswrite('result.xlsx',B5,1,'B5'); 60 | 61 | xlswrite('result.xlsx',{'R'},1,'A7'); 62 | xlswrite('result.xlsx',R,1,'B7'); 63 | 64 | xlswrite('result.xlsx',{'B'},1,'A13'); 65 | xlswrite('result.xlsx',B,1,'B13'); 66 | 67 | 68 | 69 | 70 | -------------------------------------------------------------------------------- /袁昆仑.m: -------------------------------------------------------------------------------- 1 | clc; 2 | clear; 3 | A=[1 1/5 1/3 1/3 3 7 7 1/5 1/5; 4 | 5 1 3 3 5 7 7 3 3; 5 | 3 1/3 1 1/2 5 7 7 1/3 1/3; 6 | 3 1/3 2 1 5 7 7 1/3 1/3; 7 | 1/3 1/5 1/5 1/5 1 5 5 1/5 1/5; 8 | 1/7 1/7 1/7 1/7 1/5 1 1 1/7 1/7 ; 9 | 1/7 1/7 1/7 1/7 1/5 1 1 1/7 1/7; 10 | 5 1/3 3 3 5 7 7 1 1; 11 | 5 1/3 3 3 5 7 7 1 1]; 12 | %因素对比矩阵A，只需要改变矩阵A 13 | [m,n]=size(A); %获取指标个数 14 | RI=[0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51]; 15 | R=rank(A); %求判断矩阵的秩 16 | [V,D]=eig(A); %求判断矩阵的特征值和特征向量，V特征值，D特征向量； 17 | tz=max(D); 18 | B=max(tz); %最大特征值 19 | [row, col]=find(D==B); %最大特征值所在位置 20 | C=V(:,col); %对应特征向量 21 | CI=(B-n)/(n-1); %计算一致性检验指标CI 22 | CR=CI/RI(1,n); 23 | if CR<0.10 24 | disp('CI=');disp(CI); 25 | disp('CR=');disp(CR); 26 | disp('对比矩阵A通过一致性检验，各向量权重向量Q为：'); 27 | Q=zeros(n,1); 28 | for i=1:n 29 | Q(i,1)=C(i,1)/sum(C(:,1)); %特征向量标准化 30 | end 31 | Q 32 | end -------------------------------------------------------------------------------- /论文/叶冬冬-1726062035-基于元胞自动机模拟和遗传算法改进的动态网络分配模型分析.pdf: 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-------------------------------------------------------------------------------- 1 | %调查问卷，总和为319 2 | clear 3 | 4 | N = 319; 5 | Q1 = rand(2,1); 6 | Q1 = round(N * (Q1/sum(Q1))); 7 | xlswrite('XinJie_Data.xlsx',Q1,1); 8 | Q2 = randsrc(N,1,[1 2 3 4 5;0.2 0.6 0.1 0.05 0.05]); 9 | xlswrite('XinJie_Data.xlsx',Q2,2); 10 | Q3 = rand(4,1); 11 | Q3 = round(N * (Q3/sum(Q3))); 12 | xlswrite('XinJie_Data.xlsx',Q3,3); 13 | Q4 = randsrc(N,1,[1 2 3 4 5 6;0.2 0.05 0.05 0.6 0.05 0.05]); 14 | xlswrite('XinJie_Data.xlsx',Q4,4); 15 | Q5 = randsrc(N,1,[1 2 3 4;0.7 0.1 0.1 0.1]); 16 | xlswrite('XinJie_Data.xlsx',Q5,5); 17 | Q6 = rand(6,1); 18 | Q6 = round(N * (Q6/sum(Q6))); 19 | xlswrite('XinJie_Data.xlsx',Q6,6); 20 | Q7 = rand(7,1); 21 | Q7 = round(N * (Q7/sum(Q7))); 22 | xlswrite('XinJie_Data.xlsx',Q7,7); 23 | Q8 = rand(10,1); 24 | Q8 = round(N * (Q8/sum(Q8))); 25 | xlswrite('XinJie_Data.xlsx',Q8,8); 26 | Q9 = rand(5,1); 27 | Q9 = round(N * (Q9/sum(Q9))); 28 | xlswrite('XinJie_Data.xlsx',Q9,9); 29 | Q10 = rand(5,1); 30 | Q10 = round(N * (Q10/sum(Q10))); 31 | xlswrite('XinJie_Data.xlsx',Q10,10); 32 | Q11 = rand(5,1); 33 | Q11 = round(N * (Q11/sum(Q11))); 34 | xlswrite('XinJie_Data.xlsx',Q11,11); 35 | Q12 = rand(10,1); 36 | Q12 = round(N * (Q12/sum(Q12))); 37 | xlswrite('XinJie_Data.xlsx',Q12,12); 38 | Q13 = rand(11,1); 39 | Q13 = round(N * (Q13/sum(Q13))); 40 | xlswrite('XinJie_Data.xlsx',Q13,13); 41 | Q14 = rand(3,1); 42 | Q14 = round(N * (Q14/sum(Q14))); 43 | xlswrite('XinJie_Data.xlsx',Q14,14); 44 | Q15 = rand(8,1); 45 | Q15 = round(N * (Q15/sum(Q15))); 46 | xlswrite('XinJie_Data.xlsx',Q15,15); 47 | Q16 = rand(4,1); 48 | Q16 = round(N * (Q16/sum(Q16))); 49 | xlswrite('XinJie_Data.xlsx',Q16,16); 50 | Q17 = rand(5,1); 51 | Q17 = round(N * (Q17/sum(Q17))); 52 | xlswrite('XinJie_Data.xlsx',Q17,17); 53 | Q18 = rand(4,1); 54 | Q18 = round(N * (Q18/sum(Q18))); 55 | xlswrite('XinJie_Data.xlsx',Q18,18); 56 | Q19 = rand(4,1); 57 | Q19 = round(N * (Q19/sum(Q19))); 58 | xlswrite('XinJie_Data.xlsx',Q19,19); 59 | Q20 = rand(3,1); 60 | Q20 = round(N * (Q20/sum(Q20))); 61 | xlswrite('XinJie_Data.xlsx',Q20,20); 62 | Q21 = rand(5,1); 63 | Q21 = round(N * (Q21/sum(Q21))); 64 | xlswrite('XinJie_Data.xlsx',Q21,21); 65 | Q22 = randsrc(N,1,[1 2 3 4 5 6;0.5 0.2 0.1 0.1 0.05 0.05]); 66 | xlswrite('XinJie_Data.xlsx',Q22, 22); 67 | -------------------------------------------------------------------------------- /邢捷/square.m: -------------------------------------------------------------------------------- 1 | A=xlsread('xinjie.xlsx','sheet1'); 2 | %根据自己数据存储的路径更改 3 | %第一列y，第二列x1 4 | n=length(A); 5 | y=A(:,1); 6 | x1=A(:,2); 7 | X=[ones(n,1),x1]; 8 | [b,bint,r,rint,stats]=regress(y,X); 9 | figure; 10 | set(gcf,'visible','off'); 11 | set(gcf,'outerposition',get(0,'screensize')); 12 | plot(x1,y,'rp'); 13 | rcoplot(r,rint); 14 | title('\fontname{宋体}残差图绘制'); 15 | xlabel('数据');ylabel('残差'); 16 | set(gca,'FontSize',30,'Fontname', 'Times New Roman'); 17 | set(get(gca,'XLabel'),'Fontsize',30,'Fontname', '宋体'); 18 | set(get(gca,'YLabel'),'Fontsize',30,'Fontname', '宋体'); 19 | z=b(1)+b(2)*x1; 20 | print(gcf,'-dpng','-r300','残差图绘制'); 21 | figure; 22 | set(gcf,'visible','off'); 23 | set(gcf,'outerposition',get(0,'screensize')); 24 | plot(x1,y,'rp',x1,z,'b'); 25 | title('\fontname{宋体}散点图及其回归线'); 26 | set(gca,'FontSize',30,'Fontname', 'Times New Roman'); 27 | set(get(gca,'XLabel'),'Fontsize',30,'Fontname', '宋体'); 28 | set(get(gca,'YLabel'),'Fontsize',30,'Fontname', '宋体'); 29 | print(gcf,'-dpng','-r300','散点图及其回归线'); 30 | b --------------------------------------------------------------------------------