close all;
clear all;
clc
load data.mat; % 载入数据
net = feedforwardnet([10,10]); % 创建网络，n为隐藏层神经元个数
net.divideFcn = 'dividerand'; % 随机分配
net.divideMode = 'sample'; %分割每个样本
net.divideParam.trainRatio = 70/100; % 训练集分配比例
net.divideParam.valRatio = 30/100; % 预测集分配比例
net.divideParam.testRatio = 0/100; % 测试集分配比例
net.trainFcn = 'trainlm'; % LM
net.performFcn = 'mse'; % MSE
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', 'plotregression'}; % 画图
net.trainParam.show = 5;
net.trainParam.epochs = 10000;
net.trainParam.goal = 1e-7; % 训练目标
net.trainParam.max_fail = 25; % 最大失败次数 25
inputs = inputs';
targets = targets';
% 训练网络
[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)
% 根据画图的结果，决定是否满意
figure, plotperform(tr)
figure, plottrainstate(tr)
figure, plotconfusion(targets,outputs)
figure, ploterrhist(errors)
