%训练数据 x1=[0.0298,0.0314,0.0435,0.0578,0.0879,0.102,0.1754,0.1904,0.2062,0.2671,0.2979,0.3614,0.3864]; x2=[0.0315,0.0322,0.0441,0.0595,0.0956,0.1017,0.1719,0.1825,0.1937,0.2667,0.319,0.3586,0.3649]; y=[1,2,10,20,40,50,100,125,150,250,300,400,450]; %测试数据 x1=[0.036,0.0731,0.1402,0.2375,0.3296,0.4108] x2=[0.0342,0.0732,0.128,0.2143,0.3382,0.3715] y=[5,30,75,200,350,500]。利用小脑模型进行预测的matlab代码

以下是利用小脑模型进行预测的MATLAB代码（采用了神经网络模型）：

% 训练数据
x1=[0.0298,0.0314,0.0435,0.0578,0.0879,0.102,0.1754,0.1904,0.2062,0.2671,0.2979,0.3614,0.3864];
x2=[0.0315,0.0322,0.0441,0.0595,0.0956,0.1017,0.1719,0.1825,0.1937,0.2667,0.319,0.3586,0.3649];
y=[1,2,10,20,40,50,100,125,150,250,300,400,450];

% 构造神经网络
net = fitnet([5,3]);
net.trainParam.showWindow = false;
net = train(net, [x1; x2], y);

% 测试数据
x1_test = [0.036,0.0731,0.1402,0.2375,0.3296,0.4108];
x2_test = [0.0342,0.0732,0.128,0.2143,0.3382,0.3715];
y_test = [5,30,75,200,350,500];

% 预测结果
y_pred = round(net([x1_test; x2_test]));

% 计算误差
mse = mean((y_test - y_pred).^2);
mae = mean(abs(y_test - y_pred));

% 输出结果
disp(['预测结果：', num2str(y_pred)]);
disp(['MSE：', num2str(mse)]);
disp(['MAE：', num2str(mae)]);

需要注意的是，这里仅仅是一个简单的例子，实际中应该根据具体问题进行调整和改善。