MAT之GRNN/PNN：基于GRNN、PNN两神经网络实现并比较鸢尾花(iris数据集)种类识别正确率、各个模型运行时间对比

MAT之GRNN/PNN：基于GRNN、PNN两神经网络实现并比较鸢尾花(iris数据集)种类识别正确率、各个模型运行时间对比

---

输出结果

实现代码

load iris_data.mat 

P_train = [];
T_train = [];
P_test = [];
T_test = [];
for i = 1:3
    temp_input = features((i-1)*50+1:i*50,:);
    temp_output = classes((i-1)*50+1:i*50,:);
    n = randperm(50);

    P_train = [P_train temp_input(n(1:40),:)'];
    T_train = [T_train temp_output(n(1:40),:)'];

    P_test = [P_test temp_input(n(41:50),:)'];
    T_test = [T_test temp_output(n(41:50),:)'];
end

result_grnn = [];
result_pnn = [];
time_grnn = [];
time_pnn = [];

for i = 1:4
    for j = i:4
        p_train = P_train(i:j,:);
        p_test = P_test(i:j,:);

        t = cputime; 

        net_grnn = newgrnn(p_train,T_train);

        t_sim_grnn = sim(net_grnn,p_test);
        T_sim_grnn = round(t_sim_grnn);
        t = cputime - t;
        time_grnn = [time_grnn t];
        result_grnn = [result_grnn T_sim_grnn'];

        t = cputime;
        Tc_train = ind2vec(T_train);

        net_pnn = newpnn(p_train,Tc_train);

        Tc_test = ind2vec(T_test);
        t_sim_pnn = sim(net_pnn,p_test);
        T_sim_pnn = vec2ind(t_sim_pnn);
        t = cputime - t;
        time_pnn = [time_pnn t];
        result_pnn = [result_pnn T_sim_pnn'];
    end
end

accuracy_grnn = [];
accuracy_pnn = [];
time = [];
for i = 1:10
    accuracy_1 = length(find(result_grnn(:,i) == T_test'))/length(T_test);
    accuracy_2 = length(find(result_pnn(:,i) == T_test'))/length(T_test);
    accuracy_grnn = [accuracy_grnn accuracy_1];
    accuracy_pnn = [accuracy_pnn accuracy_2];
end

result = [T_test' result_grnn result_pnn]
accuracy = [accuracy_grnn;accuracy_pnn]
time = [time_grnn;time_pnn]

相关文章
MAB之GRNN/PNN：基于GRNN、PNN两神经网络实现并比较鸢尾花种类识别正确率、各个模型运行时间对比