用于近红外光谱的BP-PLS的matlab代码

以下是一个简单的基于BP-PLS的近红外光谱预测的 Matlab 代码示例：

% 导入数据
data = importdata('spectra_data.txt'); % 光谱数据
labels = importdata('spectra_labels.txt'); % 标签数据

% 划分训练集和测试集
[train_data, test_data, train_labels, test_labels] = split_data(data, labels, 0.7);

% BP-PLS 参数设置
hidden_layer_size = 10; % 隐藏层节点数
num_PLS_components = 5; % PLS 分量数
learning_rate = 0.1; % 学习率
max_epoch = 100; % 最大迭代次数

% BP-PLS 模型训练
[net, RMSEC, Tpred] = BP_PLS(train_data, train_labels, hidden_layer_size, num_PLS_components, learning_rate, max_epoch);

% BP-PLS 模型测试
[test_pred, RMSEP] = test_BP_PLS(net, test_data, test_labels);

% 结果可视化
figure;
plot(test_labels, test_pred, 'o');
xlabel('True value');
ylabel('Predicted value');
title(sprintf('BP-PLS Prediction (RMSEP = %.4f)', RMSEP));

% 分割数据
function [train_data, test_data, train_labels, test_labels] = split_data(data, labels, train_ratio)
    num_train = round(size(data, 1) * train_ratio);
    train_data = data(1:num_train, :);
    test_data = data(num_train+1:end, :);
    train_labels = labels(1:num_train);
    test_labels = labels(num_train+1:end);
end

% BP-PLS 模型训练
function [net, RMSEC, Tpred] = BP_PLS(data, labels, hidden_layer_size, num_PLS_components, learning_rate, max_epoch)
    % PLS
    [XL, YL, XS, YS, beta, PCTVAR] = plsregress(data, labels, num_PLS_components);
    T = XS * beta';

    % BP 神经网络
    net = fitnet(hidden_layer_size);
    net.trainParam.lr = learning_rate;
    net.trainParam.epochs = max_epoch;
    [net, tr] = train(net, T', YS);

    % 计算 RMSEC 和 Tpred
    train_pred = net(T');
    RMSEC = sqrt(mean((train_pred - YS').^2));
    Tpred = T;
end

% BP-PLS 模型测试
function [test_pred, RMSEP] = test_BP_PLS(net, test_data, test_labels)
    [~, T] = plsregress(test_data, test_labels, size(net.IW{1}, 2));
    test_pred = net(T');
    RMSEP = sqrt(mean((test_pred - test_labels').^2));
end

需要注意的是，此代码仅为示例代码，实际使用中需要根据数据和模型的特点进行相应的修改和调整。