帮我修改代码中的错误% 导入数据 load fisheriris x = meas'; t = zeros(3, size(x,2)); for i = 1:size(x,2) if strcmp(species{i},'setosa') t(1,i) = 1; elseif strcmp(species{i},'versicolor') t(2,i) = 1; else t(3,i) = 1; end end % 划分训练集和测试集 [trainInd,valInd,testInd] = dividerand(size(x,2),0.6,0.2,0.2); x_train = x(:,trainInd); t_train = t(:,trainInd); x_val = x(:,valInd); t_val = t(:,valInd); x_test = x(:,testInd); t_test = t(:,testInd); % 构建BP网络 net = feedforwardnet([10,5]); net.trainFcn = 'traingd'; net.trainParam.epochs = 1000; net.trainParam.lr = 0.01; net.trainParam.goal = 0.01; net.divideFcn = ''; net = train(net,x_train,t_train); % 绘制误差曲线 figure; plot(net.trainRecord.perf); xlabel('Epochs'); ylabel('Training Error'); % 测试网络 y_test = net(x_test); [~,t_test_class] = max(t_test); [~,y_test_class] = max(y_test); confusion = confusionmat(t_test_class,y_test_class); disp('Confusion Matrix:'); disp(confusion);

你提供的代码中有一些问题，我将逐一列出并提供修改建议：

1. 代码中未定义变量 `species`，因此会在运行时出现错误。你需要在导入数据时，将 `species` 也一同导入进来。

修改建议：

load fisheriris
x = meas';
species = species'; % 将 species 转置，使其变成行向量
t = zeros(3, size(x,2));
for i = 1:size(x,2)
    if strcmp(species{i},'setosa')
        t(1,i) = 1;
    elseif strcmp(species{i},'versicolor')
        t(2,i) = 1;
    else
        t(3,i) = 1;
    end
end

2. 在构建神经网络时，你将 `net.divideFcn` 设为空，这意味着不会对数据集进行划分，因此在训练神经网络时，会出现过拟合现象。你需要将 `net.divideFcn` 设为 `'dividerand'`，并且指定训练集、验证集和测试集的比例。

修改建议：

net.divideFcn = 'dividerand';
net.divideParam.trainRatio = 0.6;
net.divideParam.valRatio = 0.2;
net.divideParam.testRatio = 0.2;

3. 在计算混淆矩阵时，你使用了 `confusionmat` 函数，但未指定类别标签。因此，混淆矩阵的行和列标签将使用默认的 1、2、3 等数字。你需要指定类别标签，以便更好地理解混淆矩阵。

修改建议：

classNames = {'setosa','versicolor','virginica'};
confusion = confusionmat(t_test_class,y_test_class,'Order',classNames);

综上所述，你可以将代码修改为以下形式：

load fisheriris
x = meas';
species = species'; % 将 species 转置，使其变成行向量
t = zeros(3, size(x,2));
for i = 1:size(x,2)
    if strcmp(species{i},'setosa')
        t(1,i) = 1;
    elseif strcmp(species{i},'versicolor')
        t(2,i) = 1;
    else
        t(3,i) = 1;
    end
end

[trainInd,valInd,testInd] = dividerand(size(x,2),0.6,0.2,0.2);
x_train = x(:,trainInd);
t_train = t(:,trainInd);
x_val = x(:,valInd);
t_val = t(:,valInd);
x_test = x(:,testInd);
t_test = t(:,testInd);

net = feedforwardnet([10,5]);
net.trainFcn = 'traingd';
net.trainParam.epochs = 1000;
net.trainParam.lr = 0.01;
net.trainParam.goal = 0.01;
net.divideFcn = 'dividerand';
net.divideParam.trainRatio = 0.6;
net.divideParam.valRatio = 0.2;
net.divideParam.testRatio = 0.2;
net = train(net,x_train,t_train);

figure;
plot(net.trainRecord.perf);
xlabel('Epochs');
ylabel('Training Error');

y_test = net(x_test);
[~,t_test_class] = max(t_test);
[~,y_test_class] = max(y_test);
classNames = {'setosa','versicolor','virginica'};
confusion = confusionmat(t_test_class,y_test_class,'Order',classNames);
disp('Confusion Matrix:');
disp(confusion);