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 x3=[0.036,0.0731,0.1402,0.2375,0.3296,0.4108] x4=[0.0342,0.0732,0.128,0.2143,0.3382,0.3715] y1=[5,30,75,200,350,500]，进行小脑模型预测的matlab代码

以下是一个利用小脑模型进行预测的 MATLAB 代码示例：

% 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
x3 = [0.036,0.0731,0.1402,0.2375,0.3296,0.4108];
x4 = [0.0342,0.0732,0.128,0.2143,0.3382,0.3715];
y1 = [5,30,75,200,350,500];

% 设定小脑模型参数
num_neurons = 50; % 小脑模型的神经元数量
input_dim = 2; % 输入数据的维度

% 初始化小脑模型
W = rand(num_neurons, input_dim); % 权重矩阵
b = rand(num_neurons, 1); % 偏置向量

% 训练小脑模型
num_epochs = 1000; % 训练次数
learning_rate = 0.1; % 学习率

for epoch = 1:num_epochs
    % 随机选择一个样本进行训练
    i = randi(length(x1));
    x = [x1(i); x2(i)];
    t = y(i);
    
    % 前向传播
    h = tanh(W * x + b);
    y_pred = h(1); % 取出第一个神经元的输出作为预测结果
    
    % 计算误差
    loss = (y_pred - t)^2;
    
    % 反向传播
    delta = 2 * (y_pred - t) * (1 - h(1)^2);
    grad_W = delta * x';
    grad_b = delta;
    
    % 更新权重和偏置
    W = W - learning_rate * grad_W;
    b = b - learning_rate * grad_b;
end

% 利用小脑模型进行预测
num_samples = length(x3);
y_pred = zeros(num_samples, 1); % 预测结果
for i = 1:num_samples
    x = [x3(i); x4(i)]; % 取出第 i 行数据
    h = tanh(W * x + b); % 计算小脑模型的输出
    y_pred(i) = h(1); % 取出第一个神经元的输出作为预测结果
end

% 绘制预测结果和真实值的比较图
plot(y_pred, '-o', 'LineWidth', 2);
hold on;
plot(y1, '-o', 'LineWidth', 2);
legend('预测值', '真实值');

在这个示例中，我们首先载入训练数据和测试数据。然后，我们设定了小脑模型的参数，并初始化了小脑模型的权重矩阵和偏置向量。接着，我们使用随机梯度下降算法对小脑模型进行训练。最后，我们利用训练好的小脑模型对测试数据进行预测，并绘制了预测结果和真实值的比较图。