close all

clear all

clc

x=xlsread('training_data.xls',['B2:G401']);

y=xlsread('training_data.xls',['I2:K401']);

inputs = x';

targets = y';

% 创建一个模式识别网络(两层BP网络)，同时给出中间层神经元的个数，这里使用20

hiddenLayerSize = 20;

net = patternnet(hiddenLayerSize);

% 对数据进行预处理，这里使用了归一化函数(一般不用修改)

% For a list of all processing functions type: help nnprocess

net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};

net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};

% 把训练数据分成三部分，训练网络、验证网络、测试网络

% For a list of all data division functions type: help nndivide

net.divideFcn = 'dividerand'; % Divide data randomly

net.divideMode = 'sample'; % Divide up every sample

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

% 训练函数

% For a list of all training functions type: help nntrain

net.trainFcn = 'trainlm'; % Levenberg-Marquardt

% 使用均方误差来评估网络

% For a list of all performance functions type: help nnperformance

net.performFcn = 'mse'; % Mean squared error

% 画图函数

% For a list of all plot functions type: help nnplot

net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...

'plotregression', 'plotfit'};

% 开始训练网络(包含了训练和验证的过程)

[net,tr] = train(net,inputs,targets);

% 测试网络

outputs = net(inputs);

errors = gsubtract(targets,outputs);

performance = perform(net,targets,outputs)

% 获得训练、验证和测试的结果

trainTargets = targets .* tr.trainMask{1};

valTargets = targets .* tr.valMask{1};

testTargets = targets .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,outputs)

valPerformance = perform(net,valTargets,outputs)

testPerformance = perform(net,testTargets,outputs)

% 可以查看网络的各个参数

view(net)

% 根据画图的结果，决定是否满意

% Uncomment these lines to enable various plots.

figure, plotperform(tr)

figure, plottrainstate(tr)

figure, plotconfusion(targets,outputs)

figure, ploterrhist(errors)

%如果你对该次训练满意，可以保存训练好网络

save('training_net.mat','net','tr');

下面是用来分类的代码

clear all

close all

clc

load 'training_net.mat'

%% You can change the filename, sheet name, and range

%导入测试数据

new_input = xlsread('new_data.xls',['A2:F25']);

new_output = round(net(new_input'));

xlswrite('new_data.xls',new_output','result','G2');

%把二进制转换成对应的类别

new_output=new_output';

[r c]=size(new_output);

my_category=zeros(r,1);

for i=1:r

my_category(i,1)=4*new_output(i,1)+2*new_output(i,2)+1*new_output(i,3);

end

xlswrite('new_data.xls',my_category,'result','J2');

%% End of Change