Matlab 基于BP神经网络的数据分类预测 BP分类

%% 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(357); P_train = res(temp(1: 240), 1: 12)'; T_train = res(temp(1: 240), 13)'; M = size(P_train, 2); P_test = res(temp(241: end), 1: 12)'; T_test = res(temp(241: end), 13)'; 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 ); %% 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 %% 训练网络 net = train(net, p_train, t_train); %% 仿真测试 t_sim1 = sim(net, p_train); t_sim2 = sim(net, p_test ); %% 数据反归一化 T_sim1 = vec2ind(t_sim1); T_sim2 = vec2ind(t_sim2); %% 数据排序 [T_train, index_1] = sort(T_train); [T_test , index_2] = sort(T_test ); T_sim1 = T_sim1(index_1); T_sim2 = T_sim2(index_2); %% 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', '预测值') xlabel('预测样本') ylabel('预测结果') string = {strcat('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', '预测值') xlabel('预测样本') ylabel('预测结果') string = {strcat('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';