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—Jason niu

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]