回归预测-基于灰狼优化BP神经网络的数据回归预测Matlab程序 GWO-BP 多特征输入单输出

%% 清空环境变量 warning off % 关闭报警信息 close all % 关闭开启的图窗 clear % 清空变量 clc % 清空命令行 %% 导入数据 res = xlsread('数据集.xlsx'); %% 数据分析 num_size = 0.7; % 训练集占数据集比例 outdim = 1; % 最后一列为输出 num_samples = size(res, 1); % 样本个数 res = res(randperm(num_samples), :); % 打乱数据集（不希望打乱时，注释该行） num_train_s = round(num_size * num_samples); % 训练集样本个数 f_ = size(res, 2) - outdim; % 输入特征维度 %% 划分训练集和测试集 P_train = res(1: num_train_s, 1: f_)'; T_train = res(1: num_train_s, f_ + 1: end)'; M = size(P_train, 2); P_test = res(num_train_s + 1: end, 1: f_)'; T_test = res(num_train_s + 1: end, f_ + 1: end)'; N = size(P_test, 2); %% 数据归一化 [p_train, ps_input] = mapminmax(P_train, 0, 1); p_test = mapminmax('apply', P_test, ps_input); [t_train, ps_output] = mapminmax(T_train, 0, 1); t_test = mapminmax('apply', T_test, ps_output); %% 网络参数设置 num_inputs = size(p_train, 1); % 输入层维度 num_hidden = 6; % 隐藏层维度 num_output = outdim; % 输出层维度 dim = (num_inputs + 1) * num_hidden + (num_hidden + 1) * num_output; % 优化参数个数 %% 建立网络 net = newff(p_train, t_train, num_hidden); %% 设置训练参数 net.trainParam.epochs = 1000; % 训练次数 net.trainParam.goal = 1e-9; % 目标误差 net.trainParam.lr = 0.01; % 学习率 net.trainParam.showWindow = 0; % 关闭窗口 %% 优化算法参数设置 SearchAgents_no = 5; % 狼群数量 Max_iteration = 30; % 最大迭代次数 lb = -1.0 * ones(1, dim); % 参数取值下界 ub = 1.0 * ones(1, dim); % 参数取值上界 %% 优化算法初始化 Alpha_pos = zeros(1, dim); % 初始化Alpha狼的位置 Alpha_score = inf; % 初始化Alpha狼的目标函数值,将其更改为-inf以解决最大化问题 Beta_pos = zeros(1, dim); % 初始化Beta狼的位置 Beta_score = inf; % 初始化Beta狼的目标函数值 ,将其更改为-inf以解决最大化问题 Delta_pos = zeros(1, dim); % 初始化Delta狼的位置 Delta_score = inf; % 初始化Delta狼的目标函数值,将其更改为-inf以解决最大化问题 %% 初始化搜索狼群的位置 Positions = initialization(SearchAgents_no, dim, ub, lb); %% 用于记录迭代曲线 Convergence_curve = zeros(1, Max_iteration); %% 循环计数器 iter = 0; %% 优化算法主循环 while iter < Max_iteration % 对迭代次数循环 for i = 1 : size(Positions, 1) % 遍历每个狼 % 返回超出搜索空间边界的搜索狼群 % 若搜索位置超过了搜索空间，需要重新回到搜索空间 Flag4ub = Positions(i, :) > ub; Flag4lb = Positions(i, :) < lb; % 若狼的位置在最大值和最小值之间，则位置不需要调整，若超出最大值，最回到最大值边界 % 若超出最小值，最回答最小值边界 Positions(i, :) = (Positions(i, :) .* (~(Flag4ub + Flag4lb))) + ub .* Flag4ub + lb .* Flag4lb; % 计算适应度函数值 X = reshape(Positions(i, :), 1, dim); fitness = (fitcal(X, num_hidden, p_train, t_train, net)); % 更新 Alpha, Beta, Delta if fitness < Alpha_score % 如果目标函数值小于Alpha狼的目标函数值 Alpha_score = fitness; % 则将Alpha狼的目标函数值更新为最优目标函数值 Alpha_pos = Positions(i, :); % 同时将Alpha狼的位置更新为最优位置 end if fitness > Alpha_score && fitness < Beta_score % 如果目标函数值介于于Alpha狼和Beta狼的目标函数值之间 Beta_score = fitness; % 则将Beta狼的目标函数值更新为最优目标函数值 Beta_pos = Positions(i, :); % 同时更新Beta狼的位置 end if fitness > Alpha_score && fitness > Beta_score && fitness < Delta_score % 如果目标函数值介于于Beta狼和Delta狼的目标函数值之间 Delta_score = fitness; % 则将Delta狼的目标函数值更新为最优目标函数值 Delta_pos = Positions(i, :); % 同时更新Delta狼的位置 end end % 线性权重递减 wa = 2 - iter * ((2) / Max_iteration); % 更新搜索狼群的位置 for i = 1 : size(Positions, 1) % 遍历每个狼 for j = 1 : size(Positions, 2) % 遍历每个维度 % 包围猎物，位置更新 r1 = rand; % r1 is a random number in [0,1] r2 = rand; % r2 is a random number in [0,1] A1 = 2 * wa * r1 - wa; % 计算系数A，Equation (3.3) C1 = 2 * r2; % 计算系数C，Equation (3.4) % Alpha 位置更新 D_alpha = abs(C1 * Alpha_pos(j) - Positions(i, j)); % Equation (3.5)-part 1 X1 = Alpha_pos(j) - A1 * D_alpha; % Equation (3.6)-part 1 r1 = rand; % r1 is a random number in [0,1] r2 = rand; % r2 is a random number in [0,1] A2 = 2 * wa * r1 - wa; % 计算系数A，Equation (3.3) C2 = 2 *r2; % 计算系数C，Equation (3.4) % Beta 位置更新 D_beta = abs(C2 * Beta_pos(j) - Positions(i, j)); % Equation (3.5)-part 2 X2 = Beta_pos(j) - A2 * D_beta; % Equation (3.6)-part 2 r1 = rand; % r1 is a random number in [0,1] r2 = rand; % r2 is a random number in [0,1] A3 = 2 *wa * r1 - wa; % 计算系数A，Equation (3.3) C3 = 2 *r2; % 计算系数C，Equation (3.4) % Delta 位置更新 D_delta = abs(C3 * Delta_pos(j) - Positions(i, j)); % Equation (3.5)-part 3 X3 = Delta_pos(j) - A3 * D_delta; % Equation (3.5)-part 3 % 位置更新 Positions(i, j) = (X1 + X2 + X3) / 3; % Equation (3.7) end end % 更新迭代器 iter = iter + 1; Convergence_curve(iter) = Alpha_score; disp(['GWO current iteration is: ',num2str(iter), ', best fitness is: ', num2str(Alpha_score)]); end %% 获取最优权值和偏置 w1 = Alpha_pos(1 : num_inputs * num_hidden); B1 = Alpha_pos(num_inputs * num_hidden + 1 : num_inputs * num_hidden + num_hidden); w2 = Alpha_pos(num_inputs * num_hidden + num_hidden + 1 : num_inputs * num_hidden ... + num_hidden + num_hidden * num_output); B2 = Alpha_pos(num_inputs * num_hidden + num_hidden + num_hidden * num_output + 1 : ... num_inputs * num_hidden + num_hidden + num_hidden * num_output + num_output); %% 网络赋值 net.Iw{1, 1} = reshape(w1, num_hidden, num_inputs); net.Lw{2, 1} = reshape(w2, num_output, num_hidden); net.b{1} = reshape(B1, num_hidden, 1); net.b{2} = B2'; %% 打开训练窗口 net.trainParam.showWindow = 1; % 打开窗口 %% 网络训练 net = train(net, p_train, t_train); %% 仿真预测 t_sim1 = sim(net, p_train); t_sim2 = sim(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); %% 均方根误差 error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M); error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N); %% 适应度曲线 figure; plot(1 : length(Convergence_curve), Convergence_curve, 'LineWidth', 1.5); title('适应度曲线', 'FontSize', 13); xlabel('迭代次数', 'FontSize', 10); ylabel('适应度值', 'FontSize', 10); xlim([1, length(Convergence_curve)]) grid %% 绘图 figure plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1) legend('真实值', '预测值') xlabel('预测样本') ylabel('预测结果') string = {'训练集预测结果对比'; ['RMSE=' num2str(error1)]}; title(string) xlim([1, M]) grid figure plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1) legend('真实值', '预测值') xlabel('预测样本') ylabel('预测结果') string = {'测试集预测结果对比'; ['RM