基于Matlab有导师学习神经网络的回归拟合-近红外光谱的汽油辛烷值预测仿真（源码+数据）.rar

%% 有导师学习神经网络的回归拟合——基于近红外光谱的汽油辛烷值预测 %% 清空环境变量 clear all clc %% 训练集/测试集产生 load spectra_data.mat % 随机产生训练集和测试集 temp = randperm(size(NIR,1)); % 训练集——50个样本 P_train = NIR(temp(1:50),:)'; T_train = octane(temp(1:50),:)'; % 测试集——10个样本 P_test = NIR(temp(51:end),:)'; T_test = octane(temp(51:end),:)'; N = size(P_test,2); %% BP神经网络创建、训练及仿真测试 % 创建网络 net = newff(P_train,T_train,9); % 设置训练参数 net.trainParam.epochs = 1000; net.trainParam.goal = 1e-3; net.trainParam.lr = 0.01; % 训练网络 net = train(net,P_train,T_train); % 仿真测试 T_sim_bp = sim(net,P_test); %% RBF神经网络创建及仿真测试 % 创建网络 net = newrbe(P_train,T_train,0.3); % 仿真测试 T_sim_rbf = sim(net,P_test); %% 性能评价 % 相对误差error error_bp = abs(T_sim_bp - T_test)./T_test; error_rbf = abs(T_sim_rbf - T_test)./T_test; % 决定系数R^2 R2_bp = (N * sum(T_sim_bp .* T_test) - sum(T_sim_bp) * sum(T_test))^2 / ((N * sum((T_sim_bp).^2) - (sum(T_sim_bp))^2) * (N * sum((T_test).^2) - (sum(T_test))^2)); R2_rbf = (N * sum(T_sim_rbf .* T_test) - sum(T_sim_rbf) * sum(T_test))^2 / ((N * sum((T_sim_rbf).^2) - (sum(T_sim_rbf))^2) * (N * sum((T_test).^2) - (sum(T_test))^2)); % 结果对比 result_bp = [T_test' T_sim_bp' T_sim_rbf' error_bp' error_rbf'] %% 绘图 figure plot(1:N,T_test,'b:*',1:N,T_sim_bp,'r-o',1:N,T_sim_rbf,'k-.^') legend('真实值','BP预测值','RBF预测值') xlabel('预测样本') ylabel('辛烷值') string = {'测试集辛烷值含量预测结果对比(BP vs RBF)';['R^2=' num2str(R2_bp) '(BP)' ' R^2=' num2str(R2_rbf) '(RBF)']}; title(string)