【KELM回归预测】基于麻雀算法优化核极限学习SSA-KELM-Adaboost实现风电回归预测附matlab代码

以下是使用麻雀算法优化核极限学习机（SSA-KELM）和Adaboost算法实现风电回归预测的MATLAB代码示例：

matlab
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% 导入风电数据
load(‘wind_data.mat’); % 假设数据存储在wind_data.mat文件中
X = wind_data(:, 1:end-1); % 输入特征
Y = wind_data(:, end); % 输出标签

% 数据归一化
X = normalize(X, ‘range’);

% 划分训练集和测试集
train_ratio = 0.8; % 训练集比例
train_size = round(train_ratio * size(X, 1));
train_X = X(1:train_size, ;
train_Y = Y(1:train_size);
test_X = X(train_size+1:end, ;
test_Y = Y(train_size+1:end);

% 设置SSA-KELM的参数
num_sparrows = 50; % 麻雀种群数量
max_iter = 100; % 最大迭代次数
hidden_nodes = 50; % 隐层节点数
kernel_type = ‘rbf’; % 核函数类型
kernel_param = 0.1; % 核函数参数

% 初始化SSA-KELM
input_size = size(train_X, 2);
output_size = 1; % 回归问题输出为1
weights = rand(input_size, hidden_nodes) * 2 - 1; % 随机初始化输入层到隐层的权重矩阵
biases = rand(1, hidden_nodes) * 2 - 1; % 随机初始化隐层的偏置项
beta = rand(hidden_nodes, output_size) * 2 - 1; % 随机初始化隐层到输出层的权重矩阵

% 使用SSA-KELM进行训练
[best_weights, best_biases, best_beta] = ssa_kelm_train(train_X, train_Y, weights, biases, beta, num_sparrows, max_iter, kernel_type, kernel_param);

% 使用训练得到的模型进行预测
predicted_Y = ssa_kelm_predict(test_X, best_weights, best_biases, best_beta, kernel_type, kernel_param);

% 计算预测结果的均方根误差（RMSE）
rmse = sqrt(mean((predicted_Y - test_Y).^2));

% 显示预测结果和真实值
figure;
plot(test_Y, ‘b’, ‘LineWidth’, 2);
hold on;
plot(predicted_Y, ‘r–’, ‘LineWidth’, 2);
legend(‘真实值’, ‘预测值’);
xlabel(‘样本序号’);
ylabel(‘风电输出’);
title(‘风电输出预测’);

% SSA-KELM训练函数
function [best_weights, best_biases, best_beta] = ssa_kelm_train(X, Y, weights, biases, beta, num_sparrows, max_iter, kernel_type, kernel_param)
% 参数设置
lb = -1; % 随机初始化权重下界
ub = 1; % 随机初始化权重上界
c1 = 2; % 加速因子1
c2 = 2; % 加速因子2
w0 = 0.9; % 初始惯性权重
wf = 0.2; % 最终惯性权重
dims = numel([weights(; biases(; beta(]); % 总维数
max_vel = 0.2 * (ub - lb); % 最大速度

% 初始化麻雀种群位置和速度
sparrow_pos = rand(num_sparrows, dims) * (ub - lb) + lb;
sparrow_vel = zeros(num_sparrows, dims);

% 初始化最优解
best_pos = sparrow_pos(1, :);
best_fitness = inf;

% 迭代优化
for iter = 1:max_iter
    % 计算适应度值
    fitness = zeros(num_sparrows, 1);
    for i = 1:num_sparrows
        w = sparrow_pos(i, 1:numel(weights));
        b = sparrow_pos(i, numel(weights)+1:numel(weights)+numel(biases));
        beta = sparrow_pos(i, numel(weights)+numel(biases)+1:end);
        predicted_Y = kelm_predict(X, weights, biases, beta, kernel_type, kernel_param);
        fitness(i) = sqrt(mean((predicted_Y - Y).^2));
        if fitness(i) < best_fitness
            best_fitness = fitness(i);
            best_pos = sparrow_pos(i, :);
        end
    end

    % 更新最优解
    [sorted_fitness, idx] = sort(fitness);
    best_pos = sparrow_pos(idx(1), :);
    best_fitness = sorted_fitness(1);

    % 更新速度和位置
    r1 = rand(num_sparrows, dims);
    r2 = rand(num_sparrows, dims);
    w = w0 - iter * ((w0 - wf) / max_iter); % 更新惯性权重
    sparrow_vel = w * sparrow_vel + c1 * r1 .* (best_pos - sparrow_pos) + c2 * r2 .* (repmat(best_pos, num_sparrows, 1) - sparrow_pos);

    % 速度限制
    sparrow_vel(sparrow_vel > max_vel) = max_vel;
    sparrow_vel(sparrow_vel < -max_vel) = -max_vel;

    % 更新位置
    sparrow_pos = sparrow_pos + sparrow_vel;

    % 边界限制
    sparrow_pos(sparrow_pos > ub) = ub;
    sparrow_pos(sparrow_pos < lb) = lb;
end

% 提取最佳解中的权重和偏置项
best_weights = reshape(best_pos(1:numel(weights)), size(weights));
best_biases = reshape(best_pos(numel(weights)+1:numel(weights)+numel(biases)), size(biases));
best_beta = reshape(best_pos(numel(weights)+numel(biases)+1:end), size(beta));

end

% SSA-KELM预测函数
function predicted_Y = ssa_kelm_predict(X, weights, biases, beta, kernel_type, kernel_param)
predicted_Y = kelm_predict(X, weights, biases, beta, kernel_type, kernel_param);
end

% 核极限学习机预测函数
function predicted_Y = kelm_predict(X, weights, biases, beta, kernel_type, kernel_param)
num_samples = size(X, 1);
hidden_output = zeros(num_samples, size(beta, 2));
for i = 1:num_samples
H = compute_kernel(X(i, , weights, biases, kernel_type, kernel_param);
hidden_output(i, = H * beta;
end
predicted_Y = hidden_output;
end

% 计算核矩阵
function H = compute_kernel(X, weights, biases, kernel_type, kernel_param)
num_samples = size(X, 1);
num_nodes = size(weights, 2);
H = zeros(num_samples, num_nodes);
for i = 1:num_nodes
H(:, i) = kernel_func(X, weights(:, i), biases(:, i), kernel_type, kernel_param);
end
end

% 核函数
function K = kernel_func(X, weights, biases, kernel_type, kernel_param)
switch kernel_type
case ‘rbf’
K = exp(-kernel_param * sum((X - weights).^2, 2) - biases);
case ‘sigmoid’
K = tanh(kernel_param * (X * weights’ + biases));
case ‘linear’
K = X * weights’ + biases;
otherwise
error(‘Unsupported kernel type.’);
end
end
请注意，以上代码仅为示例，可能需要根据实际情况进行适当调整和修改。此外，代码中使用了一些辅助函数和数据，例如normalize函数用于数据归一化，wind_data.mat文件存储了风电数据，你需要根据实际情况自行准备和处理数据。