动态神经网络时间序列预测研究.zip

%% Matlab神经网络43个案例分析 % 动态神经网络时间序列预测研究-基于MATLAB的NARX实现 % by 王小川(@王小川_matlab) % http://www.matlabsky.com % Email:[email protected] % http://weibo.com/hgsz2003 %% 清空环境变量 clear clc %% 加载数据 load phdata inputSeries = phInputs; targetSeries = phTargets; %% 建立非线性自回归模型 inputDelays = 1:2; feedbackDelays = 1:2; hiddenLayerSize = 10; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize); %% 网络数据预处理函数定义 net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'}; %% 时间序列数据准备工作 [inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries); %% 训练数据、验证数据、测试数据划分 net.divideFcn = 'dividerand'; net.divideMode = 'value'; net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; %% 网络训练函数设定 net.trainFcn = 'trainlm'; % Levenberg-Marquardt %% 误差函数设定 net.performFcn = 'mse'; % Mean squared error %% 绘图函数设定 net.plotFcns = {'plotperform','plottrainstate','plotresponse', ... 'ploterrcorr', 'plotinerrcorr'}; %% 网络训练 [net,tr] = train(net,inputs,targets,inputStates,layerStates); %% 网络测试 outputs = net(inputs,inputStates,layerStates); errors = gsubtract(targets,outputs); performance = perform(net,targets,outputs) %% 计算训练集、验证集、测试集误差 trainTargets = gmultiply(targets,tr.trainMask); valTargets = gmultiply(targets,tr.valMask); testTargets = gmultiply(targets,tr.testMask); trainPerformance = perform(net,trainTargets,outputs) valPerformance = perform(net,valTargets,outputs) testPerformance = perform(net,testTargets,outputs) %% 网络训练效果可视化 figure, plotperform(tr) figure, plottrainstate(tr) figure, plotregression(targets,outputs) figure, plotresponse(targets,outputs) figure, ploterrcorr(errors) figure, plotinerrcorr(inputs,errors) %% close loop模式的实现 % 更改NARX神经网络模式 narx_net_closed = closeloop(net); view(net) view(narx_net_closed) % 计算1500-2000个点的拟合效果 phInputs_c=phInputs(1500:2000); PhTargets_c=phTargets(1500:2000); [p1,Pi1,Ai1,t1] = preparets(narx_net_closed,phInputs_c,{},PhTargets_c); % 网络仿真 yp1 = narx_net_closed(p1,Pi1,Ai1); plot([cell2mat(yp1)' cell2mat(t1)'])