网站优化具体是怎么做的,仿知乎 wordpress,低价网站建设机构,建一个素材网站多少钱回归预测 | MATLAB实现RUN-XGBoost多输入回归预测 目录 回归预测 | MATLAB实现RUN-XGBoost多输入回归预测预测效果基本介绍程序设计参考资料 预测效果 基本介绍 MATLAB实现RUN-XGBoost多输入回归预测#xff08;完整源码和数据#xff09; 1.龙格库塔优化XGBoost#xff0c;…回归预测 | MATLAB实现RUN-XGBoost多输入回归预测 目录 回归预测 | MATLAB实现RUN-XGBoost多输入回归预测预测效果基本介绍程序设计参考资料 预测效果 基本介绍 MATLAB实现RUN-XGBoost多输入回归预测完整源码和数据 1.龙格库塔优化XGBoost数据为多输入回归数据输入7个特征输出1个变量,程序乱码是由于版本不一致导致可以用记事本打开复制到你的文件。 2.运行环境MATLAB2018b及以上。 3.附赠案例数据可直接运行main一键出图 4.注意程序和数据放在一个文件夹。 5.代码特点参数化编程、参数可方便更改、代码编程思路清晰、注释明细。 程序设计
完整源码和数据获取方式资源出下载MATLAB实现RUN-XGBoost多输入回归预测。
%% Main Loop of RUN
it1;%Number of iterations
while itMax_iterationitit1;f20.*exp(-(12.*(it/Max_iteration))); % (Eq.17.6) Xavg mean(X); % Determine the Average of SolutionsSF2.*(0.5-rand(1,pop)).*f; % Determine the Adaptive Factor (Eq.17.5)for i1:pop[~,ind_l] min(Cost);lBest X(ind_l,:); [A,B,C]RndX(pop,i); % Determine Three Random Indices of Solutions[~,ind1] min(Cost([A B C]));% Determine Delta X (Eqs. 11.1 to 11.3)gama rand.*(X(i,:)-rand(1,dim).*(ub-lb)).*exp(-4*it/Max_iteration); Stprand(1,dim).*((Best_pos-rand.*Xavg)gama);DelX 2*rand(1,dim).*(abs(Stp));% Determine Xb and Xw for using in Runge Kutta methodif Cost(i)Cost(ind1) Xb X(i,:);Xw X(ind1,:);elseXb X(ind1,:);Xw X(i,:);endSM RungeKutta(Xb,Xw,DelX); % Search Mechanism (SM) of RUN based on Runge Kutta MethodLrand(1,dim)0.5;Xc L.*X(i,:)(1-L).*X(A,:); % (Eq. 17.3)Xm L.*Best_pos(1-L).*lBest; % (Eq. 17.4)vec[1,-1];flag floor(2*rand(1,dim)1);rvec(flag); % An Interger number g 2*rand;mu 0.5.1*randn(1,dim);% Determine New Solution Based on Runge Kutta Method (Eq.18) if rand0.5Xnew (Xcr.*SF(i).*g.*Xc) SF(i).*(SM) mu.*(Xm-Xc);elseXnew (Xmr.*SF(i).*g.*Xm) SF(i).*(SM) mu.*(X(A,:)-X(B,:));end % Check if solutions go outside the search space and bring them backFUXnewub;FLXnewlb;Xnew(Xnew.*(~(FUFL)))ub.*FUlb.*FL; CostNewfobj(Xnew);if CostNewCost(i)X(i,:)Xnew;Cost(i)CostNew;end
%% Enhanced solution quality (ESQ) (Eq. 19) if rand0.5EXPexp(-5*rand*it/Max_iteration);r floor(Unifrnd(-1,2,1,1));u2*rand(1,dim); wUnifrnd(0,2,1,dim).*EXP; %(Eq.19-1)[A,B,C]RndX(pop,i);Xavg(X(A,:)X(B,:)X(C,:))/3; %(Eq.19-2) betarand(1,dim);Xnew1 beta.*(Best_pos)(1-beta).*(Xavg); %(Eq.19-3)for j1:dimif w(j)1 Xnew2(j) Xnew1(j)r*w(j)*abs((Xnew1(j)-Xavg(j))randn);elseXnew2(j) (Xnew1(j)-Xavg(j))r*w(j)*abs((u(j).*Xnew1(j)-Xavg(j))randn);endendFUXnew2ub;FLXnew2lb;Xnew2(Xnew2.*(~if randw(randi(dim)) SM RungeKutta(X(i,:),Xnew2,DelX);Xnew (Xnew2-rand.*Xnew2) SF(i)*(SM(2*rand(1,dim).*Best_pos-Xnew2)); % (Eq. 20)FUXnewub;FLXnewlb;Xnew(Xnew.*(~(FUFL)))ub.*FUlb.*FL;CostNewfobj(Xnew);if CostNewCost(i)X(i,:)Xnew;Cost(i)CostNew;endendendend
% End of ESQ
%% Determine the Best Solutionif Cost(i)Best_scoreBest_posX(i,:);Best_scoreCost(i);endend
% Save Best Solution at each iteration
curve(it) Best_score;
disp([it : num2str(it) , Best Cost num2str(curve(it) )]);endend
参考资料 [1] https://blog.csdn.net/kjm13182345320/article/details/128577926?spm1001.2014.3001.5501 [2] https://blog.csdn.net/kjm13182345320/article/details/128573597?spm1001.2014.3001.5501
