Hi Everyone,
NARX - 2hr Prediction of Cargo Temperature on transit
Pls help me out. I have experimental data for cargo temperature that I want to predict and visualize them as in a plotted graph form and have a model that will produce such when feed with different data at any point. I am struggling. I need your help
This is my code
clc clear all %% Data Import % % borderdelaysinputs - input time series. % borderdelaysoutputs_5_4tiers - feedback time series. load 'C:\Users\modelling\borderdelaysdata_5_4tiers' X = tonndata(borderdelaysinputs,false,false); T = tonndata(borderdelaysoutputs_5_4tiers,false,false);
%% Training Function trainFcn = 'trainlm'; % Levenberg-Marquardt
% Create a Nonlinear Autoregressive Network with External Input inputDelays = 1:2; feedbackDelays = 1:2; hiddenLayerSize = 10; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);
% Prepare the Data for Training and Simulation
[x,xi,ai,t] = preparets(net,X,{},T);
% 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,x,t,xi,ai);
% Test the Network y = net(x,xi,ai); e = gsubtract(t,y); performance = perform(net,t,y)
% View the Network view(net)
%% Plots % Uncomment these lines to enable various plots. %figure, plotperform(tr) %figure, plottrainstate(tr) %figure, plotregression(t,y) %figure, plotresponse(t,y) %figure, ploterrcorr(e) %figure, plotinerrcorr(x,e)
% Closed Loop Network netc = closeloop(net); netc.name = [net.name ' - Closed Loop']; view(netc) [xc,xic,aic,tc] = preparets(netc,X,{},T); yc = netc(xc,xic,aic); closedLoopPerformance = perform(netc,tc,yc)
% Step-Ahead Prediction Network nets = removedelay(net); nets.name = [net.name ' - Predict One Step Ahead']; view(nets) [xs,xis,ais,ts] = preparets(nets,X,{},T); ys = nets(xs,xis,ais); stepAheadPerformance = perform(nets,ts,ys)
I have not been able to plot the graph neither generate a model. How can I go about it?
Thank you for your assistance
