帮我写一个MATLAB的LSTM和PSO-LSTM超参数对比图的代码

好的，以下是一个简单的MATLAB代码，用于比较LSTM和PSO-LSTM的超参数：

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

% Split data into training and testing sets
train_data = data(1:800,:);
test_data = data(801:end,:);

% Define LSTM network architecture
num_features = size(train_data,2);
num_hidden_units = 10;
lstm_net = lstmLayer(num_hidden_units, 'OutputMode', 'last');
output_layer = fullyConnectedLayer(1);
regression_layer = regressionLayer;
layers = [sequenceInputLayer(num_features), lstm_net, output_layer, regression_layer];

% Train LSTM network using stochastic gradient descent
opts = trainingOptions('sgdm', 'MaxEpochs', 100, 'MiniBatchSize', 32, 'LearnRateSchedule', 'piecewise', 'LearnRateDropFactor', 0.1, 'LearnRateDropPeriod', 50);
net = trainNetwork(train_data(:,1:end-1), train_data(:,end), layers, opts);

% Use LSTM network to make predictions on testing data
lstm_predictions = predict(net, test_data(:,1:end-1));

% Define PSO-LSTM network architecture
num_particles = 10;
num_iterations = 50;
c1 = 2;
c2 = 2;
v_max = 0.1;
num_hidden_units = 10;
pso_lstm_net = psoLstmLayer(num_hidden_units, 'OutputMode', 'last', 'NumParticles', num_particles, 'MaxIterations', num_iterations, 'C1', c1, 'C2', c2, 'VelocityLimit', v_max);
output_layer = fullyConnectedLayer(1);
regression_layer = regressionLayer;
layers = [sequenceInputLayer(num_features), pso_lstm_net, output_layer, regression_layer];

% Train PSO-LSTM network using stochastic gradient descent
opts = trainingOptions('sgdm', 'MaxEpochs', 100, 'MiniBatchSize', 32, 'LearnRateSchedule', 'piecewise', 'LearnRateDropFactor', 0.1, 'LearnRateDropPeriod', 50);
net = trainNetwork(train_data(:,1:end-1), train_data(:,end), layers, opts);

% Use PSO-LSTM network to make predictions on testing data
pso_lstm_predictions = predict(net, test_data(:,1:end-1));

% Plot results
figure;
plot(test_data(:,end));
hold on;
plot(lstm_predictions);
plot(pso_lstm_predictions);
legend('True', 'LSTM', 'PSO-LSTM');

请注意，这只是一个简单的示例，可以根据您的需求进行修改。此代码需要一个名为"data.mat"的数据文件，其中包含训练和测试数据。