写一个利用BP网络对鸢尾花的分类的并能拟合相应曲线的Matlab代码

好的，以下是一个利用BP网络对鸢尾花分类并能拟合相应曲线的Matlab代码，可以输出训练过程中的误差曲线和测试结果的混淆矩阵。

% 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);

% 绘制分类决策曲线
x1range = min(x(1,:)):0.1:max(x(1,:));
x2range = min(x(2,:)):0.1:max(x(2,:));
[X1,X2] = meshgrid(x1range,x2range);
Y = zeros(size(X1));
for i = 1:numel(X1)
    xi = [X1(i); X2(i)];
    yi = net(xi);
    [~,yi_class] = max(yi);
    Y(i) = yi_class;
end
figure;
gscatter(x(1,:),x(2,:),species);
hold on;
contour(X1,X2,Y,'LineWidth',2);
xlabel('Sepal length');
ylabel('Sepal width');
legend('setosa','versicolor','virginica','Decision Boundary');

需要注意的是，这个代码中同样使用了梯度下降算法进行训练，但实际上更好的训练算法应该是Adam或者L-BFGS等。同时，绘制分类决策曲线需要对输入空间进行网格化，这样才能在二维平面上绘制出决策边界。