免费北京网站建设,wordpress 源码整合,grimhelm wordpress,适合0基础网站开发软件多输入多输出 | Matlab实现OOA-BP鱼鹰算法优化BP神经网络多输入多输出预测 目录 多输入多输出 | Matlab实现OOA-BP鱼鹰算法优化BP神经网络多输入多输出预测预测效果基本介绍程序设计往期精彩参考资料 预测效果 基本介绍 多输入多输出 | Matlab实现OOA-BP鱼鹰算法优化BP神经网络…多输入多输出 | Matlab实现OOA-BP鱼鹰算法优化BP神经网络多输入多输出预测 目录 多输入多输出 | Matlab实现OOA-BP鱼鹰算法优化BP神经网络多输入多输出预测预测效果基本介绍程序设计往期精彩参考资料 预测效果 基本介绍 多输入多输出 | Matlab实现OOA-BP鱼鹰算法优化BP神经网络多输入多输出预测 1.data为数据集10个输入特征3个输出变量。 2.main.m为主程序文件。 3.命令窗口输出MBE、MAE和R2可在下载区获取数据和程序内容。 程序设计
完整程序和数据下载方式私信博主回复Matlab实现OOA-BP鱼鹰算法优化BP神经网络多输入多输出预测。
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------function[Best_score,Best_pos,OOA_curve]OOA(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness)
lowerboundones(1,dimension).*(lowerbound); % Lower limit for variables
upperboundones(1,dimension).*(upperbound); % Upper limit for variables%% INITIALIZATION
for i1:dimensionX(:,i) lowerbound(i)rand(SearchAgents,1).*(upperbound(i) - lowerbound(i)); % Initial population
endfor i 1:SearchAgentsLX(i,:);fit(i)fitness(L);
end
%%for t1:Max_iterations % algorithm iteration%% update: BEST proposed solution[Fbest , blocation]min(fit);if t1xbestX(blocation,:); % Optimal locationfbestFbest; % The optimization objective functionelseif FbestfbestfbestFbest;xbestX(blocation,:);end%%%%for i1:SearchAgents%% Phase 1: : POSITION IDENTIFICATION AND HUNTING THE FISH (EXPLORATION)fish_positionfind(fitfit(i));% Eq(4)if size(fish_position,2)0selected_fishxbest;elseif rand 0.5selected_fishxbest;elsekrandperm(size(fish_position,2),1);selected_fishX(fish_position(k));endend%Iround(1rand);X_new_P1X(i,:)rand(1,1).*(selected_fish-I.*X(i,:));%Eq(5)X_new_P1 max(X_new_P1,lowerbound);X_new_P1 min(X_new_P1,upperbound);% update position based on Eq (6)LX_new_P1;fit_new_P1fitness(L);if fit_new_P1fit(i)X(i,:) X_new_P1;fit(i) fit_new_P1;end%% END Phase 1%%%% PHASE 2: CARRYING THE FISH TO THE SUITABLE POSITION (EXPLOITATION)X_new_P1X(i,:)(lowerboundrand*(upperbound-lowerbound))/t;%Eq(7)X_new_P1 max(X_new_P1,lowerbound);X_new_P1 min(X_new_P1,upperbound);% update position based on Eq (8)LX_new_P1;fit_new_P1fitness(L);if fit_new_P1fit(i)X(i,:) X_new_P1;fit(i) fit_new_P1;end%% END Phase 2%%end%%best_so_far(t)fbest;average(t) mean (fit);
往期精彩 MATLAB实现RBF径向基神经网络多输入多输出预测 MATLAB实现BP神经网络多输入多输出预测 MATLAB实现DNN神经网络多输入多输出预测 参考资料 [1] https://blog.csdn.net/kjm13182345320/article/details/116377961 [2] https://blog.csdn.net/kjm13182345320/article/details/127931217 [3] https://blog.csdn.net/kjm13182345320/article/details/127894261
