你能帮我用Matlab语言写一段PSO优化LSTM并用于多输入单输出的预测代码吗
时间: 2023-03-30 12:02:32 浏览: 81
可以的,以下是代码:
% PSO优化LSTM并用于多输入单输出的预测
% 导入数据
data = csvread('data.csv');
X = data(:, 1:3); % 输入数据
Y = data(:, 4); % 输出数据
% 定义LSTM网络
numFeatures = size(X, 2); % 输入特征数
numHiddenUnits = 200; % 隐藏单元数
numResponses = 1; % 输出响应数
layers = [ ...
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits,'OutputMode','last')
fullyConnectedLayer(numResponses)
regressionLayer];
% 定义PSO参数
numVariables = numHiddenUnits;
lb = -10*ones(1,numVariables);
ub = 10*ones(1,numVariables);
options = optimoptions('particleswarm','SwarmSize',50,'MaxIterations',100);
% 定义适应度函数
fitnessFcn = @(x)lstmFitnessFcn(X,Y,layers,x);
% 运行PSO算法
[x,~,~,~] = particleswarm(fitnessFcn,numVariables,lb,ub,options);
% 训练LSTM网络
net = trainNetwork(X,Y,layers,trainOpts);
% 预测
YPred = predict(net,X);
% 绘制结果
plot(Y);
hold on;
plot(YPred);
legend('真实值','预测值');
function mse = lstmFitnessFcn(X,Y,layers,x)
% 训练LSTM网络并计算MSE
numHiddenUnits = length(x);
layers(2) = lstmLayer(numHiddenUnits,'OutputMode','last');
net = trainNetwork(X,Y,layers,trainOpts);
YPred = predict(net,X);
mse = mean((YPred-Y).^2);
end
function trainOpts = lstmTrainOpts()
% 定义LSTM网络训练选项
trainOpts = trainingOptions('adam', ...
'MaxEpochs',100, ...
'GradientThreshold',1, ...
'InitialLearnRate',.01, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropFactor',.1, ...
'LearnRateDropPeriod',50, ...
'Verbose',, ...
'Plots','training-progress');
end
end