Matlab 基于BP神经网络的数据分类预测 BP分类
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1. Matlab实现BP神经网络的数据分类预测（完整源码和数据)2. 多变量输入，单变量输出（类别），数据分类预测3. 评价指标包括：准确率 和 混淆矩阵4. 包括拟合效果图 和 混淆矩阵5. Excel数据，要求 Matlab 2018B及以上版本 %% 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 = {strcat('训练集预测结果对比：', ['准确率=' num2str(error1) '%'])};title(string)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 = {strcat('测试集预测结果对比：', ['准确率=' num2str(error2) '%'])};title(string)grid%% 混淆矩阵figurecm = confusionchart(T_train, T_sim1);cm.Title = 'Confusion Matrix for Train Data';cm.ColumnSummary = 'column-normalized';cm.RowSummary = 'row-normalized'; figurecm = confusionchart(T_test, T_sim2);cm.Title = 'Confusion Matrix for Test Data';cm.ColumnSummary = 'column-normalized';cm.RowSummary = 'row-normalized';