回归预测 | MATLAB实现WOA-DNN鲸鱼算法优化深度神经网络的数据多输入单输出回归预测-编程知识

回归预测 | MATLAB实现WOA-DNN鲸鱼算法优化深度神经网络的数据多输入单输出回归预测

效果一览

基本介绍

回归预测 | MATLAB实现WOA-DNN鲸鱼算法优化深度神经网络的数据多输入单输出回归预测
MATLAB实现WOA-DNN鲸鱼算法优化深度神经网络的数据多输入单输出回归预测（Matlab完整程序和数据）
输入7个特征，输出1个，即多输入单输出；优化参数为学习率，批大小，正则化系数。
运行环境Matlab2018及以上,运行主程序main即可，其余为函数文件无需运行,所有程序放在一个文件夹,data为数据集；
命令窗口输出RMSE、MAE、R2、MAPE。

程序设计

完整程序和数据下载方式1(订阅《DNN深度神经网络》专栏，同时可阅读《DNN深度神经网络》专栏收录的所有内容，数据订阅后私信我获取)：MATLAB实现WOA-DNN鲸鱼算法优化深度神经网络的数据多输入单输出回归预测
完整程序和数据下载方式2(订阅《智能学习》专栏，同时获取《智能学习》专栏收录程序3份，数据订阅后私信我获取)：MATLAB实现WOA-DNN鲸鱼算法优化深度神经网络的数据多输入单输出回归预测

%%  记录最佳参数
Best_pos(1, 2) = round(Best_pos(1, 2));
best_lr = Best_pos(1, 1);
best_hd = Best_pos(1, 2);
best_l2 = Best_pos(1, 3);%%  建立模型
% ----------------------  修改模型结构时需对应修改fical.m中的模型结构  --------------------------
layers = [sequenceInputLayer(f_)            % 输入层fullyConnectedLayer(outdim)       % 输出回归层regressionLayer];%%  参数设置
% ----------------------  修改模型参数时需对应修改fical.m中的模型参数  --------------------------
options = trainingOptions('adam', ...           % Adam 梯度下降算法'MaxEpochs', 500, ...                  % 最大训练次数 500'InitialLearnRate', best_lr, ...       % 初始学习率 best_lr'LearnRateSchedule', 'piecewise', ...  % 学习率下降'LearnRateDropFactor', 0.5, ...        % 学习率下降因子 0.1'LearnRateDropPeriod', 400, ...        % 经过 400 次训练后 学习率为 best_lr * 0.5'Shuffle', 'every-epoch', ...          % 每次训练打乱数据集'ValidationPatience', Inf, ...         % 关闭验证'L2Regularization', best_l2, ...       % 正则化参数'Plots', 'training-progress', ...      % 画出曲线'Verbose', false);%%  训练模型
net = trainNetwork(p_train, t_train, layers, options);%%  仿真验证
t_sim1 = predict(net, p_train);
t_sim2 = predict(net, p_test );%%  数据反归一化
T_sim1 = mapminmax('reverse', t_sim1, ps_output);
T_sim2 = mapminmax('reverse', t_sim2, ps_output);
T_sim1=double(T_sim1);
T_sim2=double(T_sim2);
%%  均方根误差
error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M);
error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N);
%_________________________________________________________________________%
% The Whale Optimization Algorithm
function [Best_Cost,Best_pos,curve]=WOA(pop,Max_iter,lb,ub,dim,fobj)% initialize position vector and score for the leader
Best_pos=zeros(1,dim);
Best_Cost=inf; %change this to -inf for maximization problems%Initialize the positions of search agents
Positions=initialization(pop,dim,ub,lb);curve=zeros(1,Max_iter);t=0;% Loop counter% Main loop
while t<Max_iterfor i=1:size(Positions,1)% Return back the search agents that go beyond the boundaries of the search spaceFlag4ub=Positions(i,:)>ub;Flag4lb=Positions(i,:)<lb;Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;% Calculate objective function for each search agentfitness=fobj(Positions(i,:));% Update the leaderif fitness<Best_Cost % Change this to > for maximization problemBest_Cost=fitness; % Update alphaBest_pos=Positions(i,:);endenda=2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)% a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)a2=-1+t*((-1)/Max_iter);% Update the Position of search agents for i=1:size(Positions,1)r1=rand(); % r1 is a random number in [0,1]r2=rand(); % r2 is a random number in [0,1]A=2*a*r1-a;  % Eq. (2.3) in the paperC=2*r2;      % Eq. (2.4) in the paperb=1;               %  parameters in Eq. (2.5)l=(a2-1)*rand+1;   %  parameters in Eq. (2.5)p = rand();        % p in Eq. (2.6)for j=1:size(Positions,2)if p<0.5   if abs(A)>=1rand_leader_index = floor(pop*rand()+1);X_rand = Positions(rand_leader_index, :);D_X_rand=abs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)Positions(i,j)=X_rand(j)-A*D_X_rand;      % Eq. (2.8)elseif abs(A)<1D_Leader=abs(C*Best_pos(j)-Positions(i,j)); % Eq. (2.1)Positions(i,j)=Best_pos(j)-A*D_Leader;      % Eq. (2.2)endelseif p>=0.5distance2Leader=abs(Best_pos(j)-Positions(i,j));% Eq. (2.5)Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Best_pos(j);endendendt=t+1;curve(t)=Best_Cost;[t Best_Cost]
end