给我基于鲸鱼算法(WOA)优化径向基神经网络(WOA-RBF)的分类预测的MATLAB代码

好的，以下是基于鲸鱼算法(WOA)优化径向基神经网络(WOA-RBF)的分类预测的 MATLAB 代码：

% 读取数据集
load iris_dataset.mat;

% 数据预处理
inputs = irisInputs';
targets = irisTargets';

% 参数设置
N = size(inputs, 1); % 样本数
D = size(inputs, 2); % 特征数
K = size(targets, 2); % 类别数
M = 10; % 隐含层神经元数量
sigma_max = 1; % 高斯径向基函数的最大半径
sigma_min = 0.1; % 高斯径向基函数的最小半径
max_iter = 100; % 最大迭代次数
pop_size = 20; % 种群大小
a = 2; % 参数a
c_max = 1; % 最大步长因子
c_min = 0; % 最小步长因子

% 初始化径向基函数中心
rbf_center = datasample(inputs, M, 'Replace', false);

% 初始化径向基函数半径
rbf_sigma = linspace(sigma_max, sigma_min, M);

% 初始化权值矩阵
W = randn(M, K);

% 初始化鲸鱼位置
X = zeros(pop_size, M * D + M + K);

% 初始化最优鲸鱼位置
X_best = zeros(1, M * D + M + K);

% 初始化最优适应度值
fit_best = Inf;

% 初始化步长因子
c = c_max - linspace(0, 1, max_iter) * (c_max - c_min);

% WOA算法主体部分
for iter = 1:max_iter
    for i = 1:pop_size
        % 计算适应度值
        rbf_output = zeros(N, M);
        for m = 1:M
            rbf_output(:, m) = exp(-sum((inputs - rbf_center(m, :)).^2, 2) / (2 * rbf_sigma(m)^2));
        end
        output = rbf_output * W;
        fit = 1 / N * sum(sum((targets - output).^2));
        
        % 更新最优鲸鱼位置和最优适应度值
        if fit < fit_best
            fit_best = fit;
            X_best = X(i, :);
        end
        
        % 随机选择两个鲸鱼
        r1 = randi([1, pop_size]);
        r2 = randi([1, pop_size]);
        
        % 计算向量D
        D = abs(X(r1, :) - X(r2, :));
        
        % 更新位置
        if rand() < 0.5
            X(i, :) = X_best - c(iter) * D;
        else
            X(i, :) = X_best + c(iter) * D;
        end
        
        % 边界处理
        X(i, 1:M * D) = max(min(X(i, 1:M * D), repmat(reshape(inputs', 1, []), pop_size, 1)), repmat(reshape(inputs', 1, []), pop_size, 1));
        X(i, M * D + 1:M * D + M) = max(min(X(i, M * D + 1:M * D + M), repmat(reshape(rbf_sigma', 1, []), pop_size, 1)), repmat(reshape(rbf_sigma', 1, []), pop_size, 1));
        X(i, M * D + M + 1:end) = max(min(X(i, M * D + M + 1:end), repmat(reshape(W', 1, []), pop_size, 1)), repmat(reshape(W', 1, []), pop_size, 1));
    end
    
    % 更新径向基函数中心和半径
    rbf_center = reshape(X_best(1:M * D), M, D);
    rbf_sigma = X_best(M * D + 1:M * D + M);
    
    % 更新权值矩阵
    W = reshape(X_best(M * D + M + 1:end), M, K);
end

% 测试模型
rbf_output = zeros(N, M);
for m = 1:M
    rbf_output(:, m) = exp(-sum((inputs - rbf_center(m, :)).^2, 2) / (2 * rbf_sigma(m)^2));
end
output = rbf_output * W;
[~, predict] = max(output, [], 2);
[~, label] = max(targets, [], 2);
accuracy = sum(predict == label) / N;
disp(['Accuracy: ', num2str(accuracy)]);

% 绘制决策边界
figure;
gscatter(inputs(:, 1), inputs(:, 2), label);
hold on;
x1range = min(inputs(:, 1)):0.01:max(inputs(:, 1));
x2range = min(inputs(:, 2)):0.01:max(inputs(:, 2));
[xx1, xx2] = meshgrid(x1range, x2range);
XGrid = [xx1(:) xx2(:)];
rbf_output = zeros(length(XGrid), M);
for m = 1:M
    rbf_output(:, m) = exp(-sum((XGrid - repmat(rbf_center(m, :), length(XGrid), 1)).^2, 2) / (2 * rbf_sigma(m)^2));
end
YGrid = rbf_output * W;
[~, predictions] = max(YGrid, [], 2);
gscatter(XGrid(:, 1), XGrid(:, 2), predictions, 'k', '.', 20);
title('WOA-RBF Decision Boundary');
legend('setosa', 'versicolor', 'virginica', 'Location', 'best');

注意：该代码仅供参考，实际使用时需要根据具体情况进行一定的修改。