基于Matlab实现人工神经网络案例（源码+数据）.rar

%-------------------------------------------------------------------------- % 第10章 神经网络方法 %-------------------------------------------------------------------------- %% examp10.5-1 BP网络数据拟合 HeadData = xlsread('头围.xls'); x = HeadData(:, 4)'; y = HeadData(:, 9)'; rng(0) net = fitnet(3); trainedNet = train(net,x,y); view(trainedNet) xnew = linspace(0,18,50); ynew = trainedNet(xnew); plot(x,y,'.',xnew,ynew,'k') xlabel('年龄(x)'); ylabel('头围(y)'); trainedNet.IW{1} trainedNet.LW{2,1} trainedNet.b %% examp10.6-1 SOM网络聚类 % 1. 读取数据 [data,TextData] = xlsread('2016各地区月平均气温.xls','A2:M32'); ObsLabel = TextData(:,1); data = data'; % 2. 创建SOM网络进行聚类 net = selforgmap([3,1]); trainedNet = train(net,data); view(trainedNet) plotsomtop(trainedNet) y = trainedNet(data) % 3. 查看聚类结果 classid = vec2ind(y); ObsLabel(classid == 1) % 查看第一类中包含的城市 ObsLabel(classid == 2) % 查看第二类中包含的城市 ObsLabel(classid == 3) % 查看第三类中包含的城市 %% examp10.7-1 BP网络模式识别 [data1,textdata1] = xlsread('神经元分类识别.xlsx','附录A'); [data2,textdata2] = xlsread('神经元分类识别.xlsx','附录B'); [data3,textdata3] = xlsread('神经元分类识别.xlsx','附录C'); trainData = data1(:,3:end)'; n1 = size(trainData,2); trainGroup = textdata1(2:end,2); [Gid,Gname] = grp2idx(trainGroup); Gid = full(ind2vec(Gid')); net = patternnet(41); net.divideParam.trainRatio = 85/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 0/100; sampleData = data2(:,3:end)'; n2 = size(sampleData,2); testData = data3(:,3:end)'; n3 = size(testData,2); m = 20; trainResult = zeros(7,n1,m); sampleResult = zeros(7,n2,m); testResult = zeros(7,n3,m); for i = 1:m trainedNet = train(net,trainData,Gid); trainResult(:,:,i) = trainedNet(trainData); sampleResult(:,:,i) = trainedNet(sampleData); testResult(:,:,i) = trainedNet(testData); end trainResult = mean(trainResult,3); sampleResult = mean(sampleResult,3); testResult = mean(testResult,3); plotconfusion(Gid,trainResult) testGroup = Gname(vec2ind(testResult)) sampleGroup = Gname(vec2ind(sampleResult))