用主成分优化神经网络算法的matlab代码

以下是使用主成分分析(PCA)优化神经网络的MATLAB代码示例：

% 读取数据集
load iris_dataset.mat;
inputs = irisInputs;
targets = irisTargets;

% 数据预处理
inputs = zscore(inputs);

% 使用PCA降维
[coefficients,~,~,~,explained] = pca(inputs);
numComponents = find(cumsum(explained)/sum(explained) >= 0.95, 1);
inputs = inputs * coefficients(:,1:numComponents);

% 分割数据集
[trainInd,valInd,testInd] = divideblock(size(inputs,1),0.6,0.2,0.2);
trainInputs = inputs(trainInd,:);
trainTargets = targets(trainInd,:);
valInputs = inputs(valInd,:);
valTargets = targets(valInd,:);
testInputs = inputs(testInd,:);
testTargets = targets(testInd,:);

% 创建神经网络
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize);

% 训练神经网络
net.trainFcn = 'trainscg';
net.performFcn = 'crossentropy';
net.trainParam.showWindow = false;
[net,tr] = train(net,trainInputs',trainTargets');

% 评估神经网络
trainOutputs = net(trainInputs');
trainAccuracy = sum(round(trainOutputs) == trainTargets')/size(trainTargets,1);
valOutputs = net(valInputs');
valAccuracy = sum(round(valOutputs) == valTargets')/size(valTargets,1);
testOutputs = net(testInputs');
testAccuracy = sum(round(testOutputs) == testTargets')/size(testTargets,1);

disp(['训练准确率: ' num2str(trainAccuracy)]);
disp(['验证准确率: ' num2str(valAccuracy)]);
disp(['测试准确率: ' num2str(testAccuracy)]);

代码中使用了PCA来降维，保留了解释方差的95%。然后，将数据集分为训练集、验证集和测试集，并使用`patternnet`函数创建一个具有10个隐藏层神经元的神经网络。`train`函数用于训练神经网络，并使用`trainscg`算法和`crossentropy`性能函数。最后，通过将网络应用于训练集、验证集和测试集来评估其性能。