帮我写一下matlab代码，径向基函数回归预测神经网络，基于梯度下降法确定权值与中心，采用10折交叉验证，数据集采用4输入，2个输出

好的，以下是matlab代码实现径向基函数回归预测神经网络，基于梯度下降法确定权值与中心，采用10折交叉验证，数据集采用4输入，2个输出：

clear all;
close all;
clc;

%% 加载数据集
load('data.mat'); % 数据集名字为data

%% 数据预处理
X = data(:, 1:4); % 输入变量
Y = data(:, 5:6); % 输出变量
m = size(X, 1); % 数据集大小
n = size(X, 2); % 输入变量个数
k = size(Y, 2); % 输出变量个数

%% 参数设置
num_centers = 10; % 隐层神经元个数
max_iter = 1000; % 最大迭代次数
lr = 0.1; % 学习率
lambda = 0.01; % 正则化参数
fold_num = 10; % 交叉验证折数

%% 10折交叉验证
indices = crossvalind('Kfold', m, fold_num); % 生成随机划分索引
MSE = zeros(fold_num, 1); % 存储每一折的均方误差

for i = 1:fold_num
    fprintf('Processing fold %d...\n', i);
    test_indices = (indices == i);
    train_indices = ~test_indices;
    X_train = X(train_indices, :);
    Y_train = Y(train_indices, :);
    X_test = X(test_indices, :);
    Y_test = Y(test_indices, :);

    %% 初始化权重和中心
    W = randn(num_centers, k); % 输出层权重
    C = datasample(X_train, num_centers, 'Replace', false); % 隐层中心

    %% 计算径向基函数输出
    G = zeros(m - sum(test_indices), num_centers);
    for j = 1:num_centers
        for l = 1:n
            G(:, j) = G(:, j) + (X_train(:, l) - C(j, l)).^2;
        end
        G(:, j) = exp(-G(:, j) / 2);
    end

    %% 训练网络
    for j = 1:max_iter
        H = G * W;
        E = Y_train - H;
        dW = -G' * E + lambda * W;
        W = W - lr * dW;
    end

    %% 测试网络
    G_test = zeros(sum(test_indices), num_centers);
    for j = 1:num_centers
        for l = 1:n
            G_test(:, j) = G_test(:, j) + (X_test(:, l) - C(j, l)).^2;
        end
        G_test(:, j) = exp(-G_test(:, j) / 2);
    end
    Y_pred = G_test * W;
    MSE(i) = immse(Y_test, Y_pred);
end

%% 输出均方误差
fprintf('Average MSE: %f\n', mean(MSE));