这篇文章主要介绍“GRNN与PNN实例对比分析”,在日常操作中,相信很多人在GRNN与PNN实例对比分析问题上存在疑惑,小编查阅了各式资料,整理出简单好用的操作方法,希望对大家解答”GRNN与PNN实例对比分析”的疑惑有所帮助!接下来,请跟着小编一起来学习吧!
%% 清空环境变量
clear
clc
%% 训练集/测试集产生
% 导入数据
load iris_data.mat
% 随机产生训练集和测试集
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);
% 训练集——120个样本
P_train = [P_train temp_input(n(1:40),:)'];
T_train = [T_train temp_output(n(1:40),:)'];
% 测试集——30个样本
P_test = [P_test temp_input(n(41:50),:)'];
T_test = [T_test temp_output(n(41:50),:)'];
end
%% 模型建立
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,:);
%% GRNN创建及仿真测试
t = cputime;
% 创建网络
net_grnn = newgrnn(p_train,T_train);
% 仿真测试
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'];
%% PNN创建及仿真测试
t = cputime;
Tc_train = ind2vec(T_train);
% 创建网络
net_pnn = newpnn(p_train,Tc_train);
% 仿真测试
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
%% 性能评价
% 正确率accuracy
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
% 结果对比
result = [T_test' result_grnn result_pnn];
accuracy = [accuracy_grnn;accuracy_pnn];
time = [time_grnn;time_pnn];
%% 绘图
figure(1)
plot(1:30,T_test,'bo',1:30,result_grnn(:,4),'r-*',1:30,result_pnn(:,4),'k:^')
grid on
xlabel('测试集样本编号')
ylabel('测试集样本类别')
string = {'测试集预测结果对比(GRNN vs PNN)';['正确率:' num2str(accuracy_grnn(4)*100) '%(GRNN) vs ' num2str(accuracy_pnn(4)*100) '%(PNN)']};
title(string)
legend('真实值','GRNN预测值','PNN预测值')
figure(2)
plot(1:10,accuracy(1,:),'r-*',1:10,accuracy(2,:),'b:o')
grid on
xlabel('模型编号')
ylabel('测试集正确率')
title('10个模型的测试集正确率对比(GRNN vs PNN)')
legend('GRNN','PNN')
figure(3)
plot(1:10,time(1,:),'r-*',1:10,time(2,:),'b:o')
grid on
xlabel('模型编号')
ylabel('运行时间(s)')
title('10个模型的运行时间对比(GRNN vs PNN)')
legend('GRNN','PNN')
到此,关于“GRNN与PNN实例对比分析”的学习就结束了,希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习,快去试试吧!若想继续学习更多相关知识,请继续关注亿速云网站,小编会继续努力为大家带来更多实用的文章!
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