【BP分类】基于matlab BP神经网络结合Adaboost数据分类预测【含Matlab源码 3768期】.zip

%% 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 %% 读取数据 res = xlsread('数据集.xlsx'); %% 分析数据 num_class = length(unique(res(:, end))); % 类别数（Excel最后一列放类别） num_dim = size(res, 2) - 1; % 特征维度 num_res = size(res, 1); % 样本数（每一行，是一个样本） num_size = 0.7; % 训练集占数据集的比例 res = res(randperm(num_res), :); % 打乱数据集（不打乱数据时，注释该行） flag_conusion = 1; % 标志位为1，打开混淆矩阵（要求2018版本及以上） %% 设置变量存储数据 P_train = []; P_test = []; T_train = []; T_test = []; %% 划分数据集 for i = 1 : num_class mid_res = res((res(:, end) == i), :); % 循环取出不同类别的样本 mid_size = size(mid_res, 1); % 得到不同类别样本个数 mid_tiran = round(num_size * mid_size); % 得到该类别的训练样本个数 P_train = [P_train; mid_res(1: mid_tiran, 1: end - 1)]; % 训练集输入 T_train = [T_train; mid_res(1: mid_tiran, end)]; % 训练集输出 P_test = [P_test; mid_res(mid_tiran + 1: end, 1: end - 1)]; % 测试集输入 T_test = [T_test; mid_res(mid_tiran + 1: end, end)]; % 测试集输出 end %% 数据转置 P_train = P_train'; P_test = P_test'; T_train = T_train'; T_test = T_test'; %% 得到训练集和测试样本个数 M = size(P_train, 2); 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 = ind2vec(T_train); t_test = ind2vec(T_test ); %% 权重初始化 D = ones(1, M) / M; %% 参数设置 K = 10; % 弱回归器个数 H = 6; % 隐藏层节点个数 %% 创建网络， %% 建立模型 net = newff(p_train, t_train, 6); %% 设置训练参数 net.trainParam.epochs = 1000; % 最大迭代次数 net.trainParam.goal = 1e-6; % 目标训练误差 net.trainParam.lr = 0.01; % 学习率 %% 弱回归器回归 for i = 1 : K %% 训练网络 net = train(net, p_train, t_train); %% 仿真预测 t_sim1{i} = sim(net, p_train); t_sim2{i} = sim(net, p_test); %% 数据格式转换 ztrain = t_train; t_sim1{i} = double(t_sim1{i}); t_sim2{i} = double(t_sim2{i}); %% 预测误差 Error(i, :) = sum(abs(t_sim1{i} - ztrain)) / num_class; %% 调整D值 weight(i) = 0; for j = 1 : M if abs(Error(i, j)) > 0.1 weight(i) = weight(i) + D(i, j); D(i + 1, j) = D(i, j) * 1.1; else D(i + 1, j) = D(i, j); end end %% 弱分类器i权重 weight(i) = 0.5 / exp(abs(weight(i))); %% D值归一化 D(i + 1, :) = D(i + 1, :) / sum(D(i + 1, :)); end %% 强预测器预测 weight = weight / sum(weight); %% 强分类器分类结果 T_sim1 = zeros(num_class, M); T_sim2 = zeros(num_class, N); for i = 1 : K output1 = (weight(i) * t_sim1{i}); output2 = (weight(i) * t_sim2{i}); T_sim1 = output1 + T_sim1; T_sim2 = output2 + T_sim2; end %% 数据反归一化 T_sim1 = vec2ind(T_sim1); T_sim2 = vec2ind(T_sim2); %% 性能评价 error1 = sum((T_sim1 == T_train)) / M * 100 ; error2 = sum((T_sim2 == T_test )) / N * 100 ; %% 绘图 figure plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1) legend('真实值', 'BP-Adaboost预测值') xlabel('预测样本') ylabel('预测结果') string = {'训练集预测结果对比'; ['准确率=' num2str(error1) '%']}; title(string) grid figure plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1) legend('真实值', 'BP-Adaboost预测值') xlabel('预测样本') ylabel('预测结果') string = {'测试集预测结果对比'; ['准确率=' num2str(error2) '%']}; title(string) grid %% 混淆矩阵 figure cm = confusionchart(T_train, T_sim1); cm.Title = 'Confusion Matrix for Train Data'; cm.ColumnSummary = 'column-normalized'; cm.RowSummary = 'row-normalized'; figure cm = confusionchart(T_test, T_sim2); cm.Title = 'Confusion Matrix for Test Data'; cm.ColumnSummary = 'column-normalized'; cm.RowSummary = 'row-normalized';