写一个麻雀搜索算法优化极限学习机的matlab程序

由于麻雀搜索算法（SSA）是一种新兴的优化算法，而极限学习机（ELM）则是一种广泛应用于分类和回归问题的人工神经网络。因此，将SSA算法与ELM网络相结合，可以提高ELM网络的性能。

以下是一个使用SSA算法优化ELM网络的MATLAB程序：

% Load the data
load iris_dataset.mat

% Normalize the dataset
data = zscore(irisInputs);

% Set the parameters
n = size(data, 1); % Number of samples
m = size(data, 2); % Number of features
hidden_nodes = 10; % Number of hidden nodes
C = 1; % Regularization parameter

% Initialize the random weights and biases
input_weights = rand(hidden_nodes, m);
biases = rand(hidden_nodes, 1);

% Initialize the output weights
output_weights = zeros(hidden_nodes, size(irisTargets, 2));

% Set the maximum number of iterations
max_iter = 100;

% Set the initial best solution
best_sol = struct('input_weights', input_weights, 'biases', biases, 'output_weights', output_weights, 'mse', Inf);

% Set the search range and step size
range = 1;
step_size = 0.1;

% Initialize the position of the sparrows
positions = rand(hidden_nodes * m + hidden_nodes, 10);

for iter = 1:max_iter
% Evaluate the fitness of each sparrow
for i = 1:size(positions, 2)
input_weights = reshape(positions(1:hidden_nodes * m, i), hidden_nodes, m);
biases = reshape(positions(hidden_nodes * m + 1:end, i), hidden_nodes, 1);
output_weights = pinv(input_weights * data' + biases') * irisTargets;
predicted = (input_weights * data' + biases')' * output_weights;
mse = mean((predicted - irisTargets).^2);
if mse < best_sol.mse
best_sol.input_weights = input_weights;
best_sol.biases = biases;
best_sol.output_weights = output_weights;
best_sol.mse = mse;
end
end

% Update the position of each sparrow
for i = 1:size(positions, 2)
% Calculate the distance to the best solution
dist = norm(positions(:, i) - [reshape(best_sol.input_weights, [], 1); reshape(best_sol.biases, [], 1)]);

% Calculate the step size
s = step_size * rand() / (dist + eps);

% Update the position of the sparrow
positions(:, i) = positions(:, i) + s * (best_sol.input_weights(:) - positions(:, i));
positions(:, i) = positions(:, i) + s * (best_sol.biases(:) - positions(:, i));

% Apply the search range
positions(:, i) = max(min(positions(:, i), range), -range);
end
end

% Evaluate the performance of the best solution
input_weights = best_sol.input_weights;
biases = best_sol.biases;
output_weights = best_sol.output_weights;
predicted = (input_weights * data' + biases')' * output_weights;
mse = mean((predicted - irisTargets).^2);
accuracy = 1 - mse / var(irisTargets);

% Display the results
disp(['Accuracy: ', num2str(accuracy * 100), '%'])