你能帮我用Matlab语言写一段PSO优化ELM并用于时间序列预测的代码吗

可以的，以下是代码：

% PSO优化ELM并用于时间序列预测

% 导入数据
data = load('data.txt');
X = data(:, 1:end-1);
Y = data(:, end);

% 参数设置
inputSize = size(X, 2);
hiddenSize = 10;
outputSize = 1;
populationSize = 20;
maxIterations = 100;
c1 = 2;
c2 = 2;
w = .8;
vMax = .5;

% ELM模型
inputWeights = rand(inputSize, hiddenSize) * 2 - 1;
hiddenBias = rand(1, hiddenSize);
hiddenOutput = 1 ./ (1 + exp(-(X * inputWeights + repmat(hiddenBias, size(X, 1), 1))));
outputWeights = pinv(hiddenOutput) * Y;

% PSO优化
particlePosition = rand(populationSize, inputSize * hiddenSize + hiddenSize * outputSize);
particleVelocity = zeros(populationSize, inputSize * hiddenSize + hiddenSize * outputSize);
particleBestPosition = particlePosition;
particleBestFitness = inf(populationSize, 1);
globalBestPosition = particlePosition(1, :);
globalBestFitness = inf;
for i = 1:populationSize
inputWeights = reshape(particlePosition(i, 1:inputSize * hiddenSize), inputSize, hiddenSize);
hiddenBias = particlePosition(i, inputSize * hiddenSize + 1:inputSize * hiddenSize + hiddenSize);
hiddenOutput = 1 ./ (1 + exp(-(X * inputWeights + repmat(hiddenBias, size(X, 1), 1))));
outputWeights = pinv(hiddenOutput) * Y;
fitness = mean((Y - hiddenOutput * outputWeights).^2);
particleBestFitness(i) = fitness;
if fitness < globalBestFitness
globalBestPosition = particlePosition(i, :);
globalBestFitness = fitness;
end
end
for i = 1:maxIterations
for j = 1:populationSize
inputWeights = reshape(particlePosition(j, 1:inputSize * hiddenSize), inputSize, hiddenSize);
hiddenBias = particlePosition(j, inputSize * hiddenSize + 1:inputSize * hiddenSize + hiddenSize);
hiddenOutput = 1 ./ (1 + exp(-(X * inputWeights + repmat(hiddenBias, size(X, 1), 1))));
outputWeights = pinv(hiddenOutput) * Y;
fitness = mean((Y - hiddenOutput * outputWeights).^2);
if fitness < particleBestFitness(j)
particleBestPosition(j, :) = particlePosition(j, :);
particleBestFitness(j) = fitness;
end
if fitness < globalBestFitness
globalBestPosition = particlePosition(j, :);
globalBestFitness = fitness;
end
particleVelocity(j, :) = w * particleVelocity(j, :) + c1 * rand(1, inputSize * hiddenSize + hiddenSize * outputSize) .* (particleBestPosition(j, :) - particlePosition(j, :)) + c2 * rand(1, inputSize * hiddenSize + hiddenSize * outputSize) .* (globalBestPosition - particlePosition(j, :));
particleVelocity(j, particleVelocity(j, :) > vMax) = vMax;
particleVelocity(j, particleVelocity(j, :) < -vMax) = -vMax;
particlePosition(j, :) = particlePosition(j, :) + particleVelocity(j, :);
end
end

% 预测
inputWeights = reshape(globalBestPosition(1:inputSize * hiddenSize), inputSize, hiddenSize);
hiddenBias = globalBestPosition(inputSize * hiddenSize + 1:inputSize * hiddenSize + hiddenSize);
hiddenOutput = 1 ./ (1 + exp(-(X * inputWeights + repmat(hiddenBias, size(X, 1), 1))));
outputWeights = pinv(hiddenOutput) * Y;
predictedOutput = hiddenOutput * outputWeights;

% 显示结果
plot(Y, 'b');
hold on;
plot(predictedOutput, 'r');
xlabel('Time');
ylabel('Value');
legend('Actual', 'Predicted');