写一个MATLAB里粒子群算法优化LSTM回归预测的代码

好的，以下是MATLAB里粒子群优化LSTM回归预测的代码：

首先，导入数据并进行预处理：

data = xlsread('data.xlsx');
trainData = data(1:200, :);
testData = data(201:end, :);

% 归一化处理
[trainData, PS] = mapminmax(trainData');
trainData = trainData';

testData = mapminmax.apply(testData', PS)';
test_X = testData(:, 1:end-1);
test_Y = testData(:, end);

% 把训练数据分割成一系列的窗口
windowSize = 10;
X = [];
Y = [];
for i = 1:size(trainData, 1)-windowSize
    x = trainData(i:i+windowSize-1, 1:end-1);
    y = trainData(i+windowSize, end);
    X = [X x];
    Y = [Y y];
end

然后，定义LSTM模型：

inputSize = windowSize*size(trainData, 2)-1;
outputSize = 1;
numHiddenUnits = 10;

layers = [ ...
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits, 'OutputMode', 'last')
    fullyConnectedLayer(outputSize)
    regressionLayer];

options = trainingOptions('adam', ...
    'MaxEpochs', 100, ...
    'MiniBatchSize', 32, ...
    'GradientThreshold', 1, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', {test_X, test_Y}, ...
    'ValidationFrequency', 10, ...
    'Plots', 'training-progress');

接下来，定义适应度函数：

function fitness = fitnessFunction(x)
    net = trainLSTM(x);
    yPred = predict(net, X);
    fitness = rmse(Y, yPred);
end

function net = trainLSTM(x)
    layers = [ ...
        sequenceInputLayer(inputSize)
        lstmLayer(x(1), 'OutputMode', 'last')
        fullyConnectedLayer(outputSize)
        regressionLayer];

    options = trainingOptions('adam', ...
        'MaxEpochs', 50, ...
        'MiniBatchSize', 32, ...
        'GradientThreshold', 1, ...
        'Shuffle', 'every-epoch');

    net = trainNetwork(X, Y, layers, options);
end

function rmse = rmse(y_true, y_pred)
    rmse = sqrt(mean((y_true-y_pred).^2));
end

最后，定义粒子群算法：

% 参数
nParticles = 10;
nIterations = 20;
nVariables = 1;
minVelocity = -0.5;
maxVelocity = 0.5;
minPosition = 1;
maxPosition = 100;

% 初始化粒子群
particles = struct();
for i = 1:nParticles
    particles(i).position = randi([minPosition maxPosition], 1);
    particles(i).velocity = rand(1) * (maxVelocity - minVelocity) + minVelocity;
    particles(i).bestPosition = particles(i).position;
    particles(i).bestFitness = inf;
end
globalBestPosition = inf;
globalBestFitness = inf;

% 粒子群算法
for i = 1:nIterations
    % 计算适应度函数
    for j = 1:nParticles
        fitnessValue = fitnessFunction(particles(j).position);
        if fitnessValue < particles(j).bestFitness
            particles(j).bestFitness = fitnessValue;
            particles(j).bestPosition = particles(j).position;
        end
        if fitnessValue < globalBestFitness
            globalBestFitness = fitnessValue;
            globalBestPosition = particles(j).position;
        end
    end
    
    % 更新速度和位置
    for j = 1:nParticles
        particles(j).velocity = particles(j).velocity ...
            + rand(1) * (particles(j).bestPosition - particles(j).position) ...
            + rand(1) * (globalBestPosition - particles(j).position);
        particles(j).velocity = max(particles(j).velocity, minVelocity);
        particles(j).velocity = min(particles(j).velocity, maxVelocity);
        particles(j).position = particles(j).position + particles(j).velocity;
        particles(j).position = max(particles(j).position, minPosition);
        particles(j).position = min(particles(j).position, maxPosition);
    end
end

% 训练最佳LSTM模型
optimalNumHiddenUnits = globalBestPosition;
optimalNet = trainLSTM(optimalNumHiddenUnits);

% 预测测试集并计算RMSE
yPred = predict(optimalNet, test_X);
rmseValue = rmse(test_Y, yPred);

这就是MATLAB里粒子群优化LSTM回归预测的代码，可以根据实际情况进行修改和优化。