clear
clc
% 准备数据
inputs = rand(10, 100); % 100组输入，每组10个特征
outputs = rand(1, 100); % 100组输出，每组1个输出值
% 将数据分成训练集和测试集
trainRatio = 0.8;
valRatio = 0.1;
testRatio = 0.1;
trainInd, valInd, testInd\] = dividerand(100, trainRatio, valRatio, testRatio); X_train = inputs(:, trainInd); y_train = outputs(:, trainInd); X_test = inputs(:, testInd); y_test = outputs(:, testInd); % 创建前馈神经网络 hiddenLayerSize = 10; net = feedforwardnet(hiddenLayerSize); % 设置训练、验证和测试数据 net.divideParam.trainRatio = 0.8; net.divideParam.valRatio = 0.1; net.divideParam.testRatio = 0.1; % 训练神经网络 \[net, tr\] = train(net, X_train, y_train); % 查看训练结果 plotperform(tr); % 进行预测 y_pred = net(X_test); % 计算误差 errors = y_pred - y_test; % 输出误差 for i = 1:length(y_test) fprintf('Test sample %d:\\n', i); fprintf('Predicted output: %.4f\\n', y_pred(i)); fprintf('Actual output: %.4f\\n', y_test(i)); fprintf('Error: %.4f\\n\\n', errors(i)); end % 计算均方误差（MSE） mse = mean(errors.\^2); fprintf('Mean Squared Error: %.4f\\n', mse);