【BP回归预测】基于粒子群算法优化BP神经网络实现数据回归预测附matlab代码

%%  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');%%  划分训练集和测试集temp = randperm(103);P_train = res(temp(1: 80), 1: 7)';T_train = res(temp(1: 80), 8)';M = size(P_train, 2);P_test = res(temp(81: end), 1: 7)';T_test = res(temp(81: end), 8)';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);%%  节点个数inputnum  = size(p_train, 1);  % 输入层节点数hiddennum = 5;                 % 隐藏层节点数outputnum = size(t_train,1);   % 输出层节点数%%  建立网络net = newff(p_train, t_train, hiddennum);%%  设置训练参数net.trainParam.epochs     = 1000;      % 训练次数net.trainParam.goal       = 1e-6;      % 目标误差net.trainParam.lr         = 0.01;      % 学习率net.trainParam.showWindow = 0;         % 关闭窗口%%  参数初始化c1      = 4.494;       % 学习因子c2      = 4.494;       % 学习因子maxgen  =   50;        % 种群更新次数  sizepop =    5;        % 种群规模Vmax    =  1.0;        % 最大速度Vmin    = -1.0;        % 最小速度popmax  =  1.0;        % 最大边界popmin  = -1.0;        % 最小边界%%  节点总数numsum = inputnum * hiddennum + hiddennum + hiddennum * outputnum + outputnum;for i = 1 : sizepop    pop(i, :) = rands(1, numsum);  % 初始化种群    V(i, :) = rands(1, numsum);    % 初始化速度    fitness(i) = fun(pop(i, :), hiddennum, net, p_train, t_train);end%%  个体极值和群体极值[fitnesszbest, bestindex] = min(fitness);zbest = pop(bestindex, :);     % 全局最佳gbest = pop;                   % 个体最佳fitnessgbest = fitness;        % 个体最佳适应度值BestFit = fitnesszbest;        % 全局最佳适应度值%%  迭代寻优for i = 1 : maxgen    for j = 1 : sizepop                % 速度更新        V(j, :) = V(j, :) + c1 * rand * (gbest(j, :) - pop(j, :)) + c2 * rand * (zbest - pop(j, :));        V(j, (V(j, :) > Vmax)) = Vmax;        V(j, (V(j, :) < Vmin)) = Vmin;                % 种群更新        pop(j, :) = pop(j, :) + 0.2 * V(j, :);        pop(j, (pop(j, :) > popmax)) = popmax;        pop(j, (pop(j, :) < popmin)) = popmin;                % 自适应变异        pos = unidrnd(numsum);        if rand > 0.85            pop(j, pos) = rands(1, 1);        end                % 适应度值        fitness(j) = fun(pop(j, :), hiddennum, net, p_train, t_train);    end        for j = 1 : sizepop        % 个体最优更新        if fitness(j) < fitnessgbest(j)            gbest(j, :) = pop(j, :);            fitnessgbest(j) = fitness(j);        end        % 群体最优更新         if fitness(j) < fitnesszbest            zbest = pop(j, :);            fitnesszbest = fitness(j);        end    end    BestFit = [BestFit, fitnesszbest];    end%%  提取最优初始权值和阈值w1 = zbest(1 : inputnum * hiddennum);B1 = zbest(inputnum * hiddennum + 1 : inputnum * hiddennum + hiddennum);w2 = zbest(inputnum * hiddennum + hiddennum + 1 : inputnum * hiddennum ...    + hiddennum + hiddennum * outputnum);B2 = zbest(inputnum * hiddennum + hiddennum + hiddennum * outputnum + 1 : ...    inputnum * hiddennum + hiddennum + hiddennum * outputnum + outputnum);%%  最优值赋值net.Iw{1, 1} = reshape(w1, hiddennum, inputnum);net.Lw{2, 1} = reshape(w2, outputnum, hiddennum);net.b{1}     = reshape(B1, hiddennum, 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, 2)' ./ M);error2 = sqrt(sum((T_sim2 - T_test) .^2, 2)' ./ N);%%  绘图figureplot(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])gridfigureplot(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'; ['RMSE=' num2str(error2)]};title(string)xlim([1, N])grid%%  误差曲线迭代图figure;plot(1 : length(BestFit), BestFit, 'LineWidth', 1.5);xlabel('粒子群迭代次数');ylabel('适应度值');xlim([1, length(BestFit)])string = {'模型迭代误差变化'};title(string)grid on%%  相关指标计算% R2R1 = 1 - norm(T_train - T_sim1)^2 / norm(T_train - mean(T_train))^2;R2 = 1 - norm(T_test  - T_sim2)^2 / norm(T_test  - mean(T_test ))^2;disp(['训练集数据的R2为：', num2str(R1)])disp(['测试集数据的R2为：', num2str(R2)])% MAEmae1 = sum(abs(T_sim1 - T_train), 2)' ./ M ;mae2 = sum(abs(T_sim2 - T_test ), 2)' ./ N ;disp(['训练集数据的MAE为：', num2str(mae1)])disp(['测试集数据的MAE为：', num2str(mae2)])% MBEmbe1 = sum(T_sim1 - T_train, 2)' ./ M ;mbe2 = sum(T_sim2 - T_test , 2)' ./ N ;disp(['训练集数据的MBE为：', num2str(mbe1)])disp(['测试集数据的MBE为：', num2str(mbe2)])%%  绘制散点图sz = 25;c = 'b';figurescatter(T_train, T_sim1, sz, c)hold onplot(xlim, ylim, '--k')xlabel('训练集真实值');ylabel('训练集预测值');xlim([min(T_train) max(T_train)])ylim([min(T_sim1) max(T_sim1)])title('训练集预测值 vs. 训练集真实值')figurescatter(T_test, T_sim2, sz, c)hold onplot(xlim, ylim, '--k')xlabel('测试集真实值');ylabel('测试集预测值');xlim([min(T_test) max(T_test)])ylim([min(T_sim2) max(T_sim2)])title('测试集预测值 vs. 测试集真实值')