pso-lstm代码 matlab

这里是一个简单的 MATLAB 实现 PSO-LSTM 的代码，供你参考：

% PSO-LSTM Code

% Load data
load('data.mat');

% Initialize PSO parameters
num_particles = 20;
num_iterations = 100;
w = 0.729; % inertia weight
c1 = 1.49445; % cognitive weight
c2 = 1.49445; % social weight
dim = 4*hidden_size+3; % number of parameters in LSTM

% Initialize LSTM parameters
hidden_size = 10;
input_size = 1;
output_size = 1;
sigma = 0.1; % standard deviation for parameter initialization
theta = sigma*randn(dim,1); % initialize LSTM parameters

% Initialize PSO variables
v = zeros(dim,num_particles);
p = repmat(theta,1,num_particles);
p_best = p;
p_best_fitness = Inf(1,num_particles);
g_best = theta;
g_best_fitness = Inf;

% Train LSTM with PSO
for i=1:num_iterations
    % Evaluate fitness of particles
    for j=1:num_particles        
        % Get LSTM parameters from particle
        lstm_params = reshape(p(:,j),[],hidden_size+output_size);
        Wf = lstm_params(1:hidden_size, :);
        Wi = lstm_params(hidden_size+1:2*hidden_size, :);
        Wc = lstm_params(2*hidden_size+1:3*hidden_size, :);
        Wo = lstm_params(3*hidden_size+1:4*hidden_size, :);
        Wout = lstm_params(end, :);
        
        % Train LSTM on data
        [loss, ~] = train_lstm(data, Wf, Wi, Wc, Wo, Wout);
        
        % Update particle best
        if loss < p_best_fitness(j)
            p_best(:,j) = p(:,j);
            p_best_fitness(j) = loss;
        end
        
        % Update global best
        if loss < g_best_fitness
            g_best = p(:,j);
            g_best_fitness = loss;
        end
    end
    
    % Update particle velocities and positions
    for j=1:num_particles
        r1 = rand(dim,1);
        r2 = rand(dim,1);
        v(:,j) = w*v(:,j) + c1*r1.*(p_best(:,j)-p(:,j)) + c2*r2.*(g_best-p(:,j));
        p(:,j) = p(:,j) + v(:,j);
    end
end

% Evaluate LSTM on test data
lstm_params = reshape(g_best,[],hidden_size+output_size);
Wf = lstm_params(1:hidden_size, :);
Wi = lstm_params(hidden_size+1:2*hidden_size, :);
Wc = lstm_params(2*hidden_size+1:3*hidden_size, :);
Wo = lstm_params(3*hidden_size+1:4*hidden_size, :);
Wout = lstm_params(end, :);
[~, predictions] = train_lstm(test_data, Wf, Wi, Wc, Wo, Wout);

需要注意的是，这只是一个简单实现，实际应用中需要对代码进行优化和调整。