用GUI设计神经网络.rar_animalzm6_matlab_substance74s_神经网络_神经网络GUI

% Solve a Clustering Problem with a Self-Organizing Map % Script generated by NCTOOL % Created Sun Mar 11 13:48:36 CST 2012 % % This script assumes these variables are defined: % % a - input data. rng(0) % 自定义语句------------------------ a= [0.9659 0.2588 -0.2588 0.9659 -0.9659 -0.9659;... 0.2588 0.9659 0.9659 -0.2588 -0.2588 0.2588]; % --------------------------------- inputs = a; % Create a Self-Organizing Map dimension1 = 2; dimension2 = 2; net = selforgmap([dimension1 dimension2]); % Train the Network [net,tr] = train(net,inputs); % Test the Network outputs = net(inputs); % View the Network view(net) % 自定义语句---------------------------- t=0:.2:2*pi+.2; b=[cos(t);sin(t)]; y=sim(net,b); y=vec2ind(y); yu=unique(y); N=length(yu); fprintf('测试数据共分为 %d 类\n', N); for i=1:N yu_num(i)=sum(y==yu(i)); fprintf('第 %d 类包含 %d 个点\n', i, yu_num(i)); end % ------------------------------------------ % Plots % Uncomment these lines to enable various plots. %figure, plotsomtop(net) %figure, plotsomnc(net) %figure, plotsomnd(net) %figure, plotsomplanes(net) %figure, plotsomhits(net,inputs) %figure, plotsompos(net,inputs) plot(b(1,y==yu(1)),b(2,y==yu(1)),'ro'); hold on; plot(b(1,y==yu(2)),b(2,y==yu(2)),'b+'); plot(b(1,y==yu(3)),b(2,y==yu(3)),'k*'); plot(b(1,y==yu(4)),b(2,y==yu(4)),'m^'); hold off; legend('第一类','第二类','第三类','第四类');