matlab编写rbf神经

要编写RBF神经网络，需要进行以下步骤：

1. 准备数据集，包括输入数据和对应的输出数据。
2. 设计RBF神经网络的结构，包括输入层、隐含层和输出层的节点数，以及RBF函数的类型和参数。
3. 初始化网络参数，包括隐含层的权重和偏置，以及输出层的权重和偏置。
4. 训练网络，使用数据集进行反向传播算法，更新网络参数，直到达到预设的训练误差或训练次数。
5. 使用训练好的网络进行预测。

下面是一个简单的MATLAB代码示例：

% 准备数据集
x = [0 0; 0 1; 1 0; 1 1];
y = [0; 1; 1; 0];

% 设计RBF神经网络结构
inputSize = size(x, 2);
hiddenSize = 4;
outputSize = 1;
rbfType = 'gaussian';
rbfParam = 1;

% 初始化网络参数
hiddenWeight = randn(hiddenSize, inputSize);
hiddenBias = randn(hiddenSize, 1);
outputWeight = randn(outputSize, hiddenSize);
outputBias = randn(outputSize, 1);

% 训练网络
maxEpochs = 1000;
learningRate = 0.1;
targetError = 0.01;
for epoch = 1:maxEpochs
    % 前向传播
    hiddenOutput = rbf(x, hiddenWeight, hiddenBias, rbfType, rbfParam);
    output = sigmoid(outputWeight * hiddenOutput + outputBias);
    % 反向传播
    deltaOutput = (output - y) .* sigmoidDerivative(output);
    deltaHidden = (outputWeight' * deltaOutput) .* rbfDerivative(x, hiddenWeight, hiddenBias, rbfType, rbfParam);
    % 更新参数
    outputWeight = outputWeight - learningRate * deltaOutput * hiddenOutput';
    outputBias = outputBias - learningRate * deltaOutput;
    hiddenWeight = hiddenWeight - learningRate * deltaHidden * x;
    hiddenBias = hiddenBias - learningRate * deltaHidden;
    % 计算误差
    error = sum((output - y).^2) / size(x, 1);
    % 判断是否达到目标误差
    if error < targetError
        break;
    end
end

% 使用训练好的网络进行预测
hiddenOutput = rbf(x, hiddenWeight, hiddenBias, rbfType, rbfParam);
output = sigmoid(outputWeight * hiddenOutput + outputBias);
disp(output);

% RBF函数
function output = rbf(x, weight, bias, type, param)
    switch type
        case 'gaussian'
            output = exp(-pdist2(x, weight).^2 / (2 * param^2));
        case 'multiquadric'
            output = sqrt(1 + pdist2(x, weight).^2 / param^2);
        case 'inverse'
            output = 1 ./ sqrt(1 + pdist2(x, weight).^2 / param^2);
        otherwise
            error('Unknown RBF type');
    end
    output = [output; ones(1, size(output, 2))];
    output = bsxfun(@plus, weight * output, bias);
end

% RBF函数的导数
function output = rbfDerivative(x, weight, bias, type, param)
    switch type
        case 'gaussian'
            output = -pdist2(x, weight) .* rbf(x, weight, bias, type, param) / param^2;
        case 'multiquadric'
            output = pdist2(x, weight) ./ (sqrt(1 + pdist2(x, weight).^2 / param^2) * param^2);
        case 'inverse'
            output = -pdist2(x, weight) ./ (sqrt(1 + pdist2(x, weight).^2 / param^2) * param^2);
        otherwise
            error('Unknown RBF type');
    end
end

% sigmoid函数
function output = sigmoid(x)
    output = 1 ./ (1 + exp(-x));
end

% sigmoid函数的导数
function output = sigmoidDerivative(x)
    output = sigmoid(x) .* (1 - sigmoid(x));
end