www.gusucode.com > matlab神经网络原理与实例精解 本书源文件 > 第11章 用GUI设计神经网络/pr_test.m
% This script assumes these variables are defined: % % x - input data. % y0 - target data. rng(0) % 自定义语句------------------------------------ x=[0.1,4.2;-0.25,2.8;3,1.1;-0.9,1.2;-1.2,1;3.4,1;-2.5,-1.5;3,3.2;... -2.5,2.7;3.1,-3.2;4,-1.2;3.9,-1;4,3;-4,3.5]'; y=[1,1,1,1,1,2,1,2,1,2,2,2,2,1]; y0=ind2vec(y); %----------------------------------------------- inputs = x; targets = y0; % Create a Pattern Recognition Network hiddenLayerSize = 20; net = patternnet(hiddenLayerSize); % Setup Division of Data for Training, Validation, Testing net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; % Train the Network [net,tr] = train(net,inputs,targets); % Test the Network outputs = net(inputs); errors = gsubtract(targets,outputs); performance = perform(net,targets,outputs) % View the Network % view(net) % 自定义语句---------------------------------- xx=-4.4:.4:4.5; N=length(xx); for i=1:N for j=1:N xt(1,(i-1)*N+j)=xx(i); xt(2,(i-1)*N+j)=xx(j); end end yt=sim(net,xt); yt=vec2ind(yt); plot(x(1,y==2),x(2,y==2),'r>','Linewidth', 2); hold on; plot(x(1,y==1),x(2,y==1),'bo','Linewidth', 2); plot(xt(1,yt==1),xt(2,yt==1),'bo'); hold on; plot(xt(1,yt==2),xt(2,yt==2),'r>'); %-----------------------------------------------