MATLAB Answers

1

Where is output for this net ??

Asked by briget spaniel on 12 May 2016
Latest activity Edited by Staffan
on 12 May 2016
Im new in matlab now is day 2 i use matlab..
i use input 100 time series data on this.
i want get output from net at time 96 97 98 99 100 & predicted data 101 102 103 104 **4 before and 4 after 100
here the code
% Solve an Autoregression Problem with External Input with a NARX Neural Network
% Script generated by Neural Time Series app
% Created 12-May-2016 20:10:18
%
% This script assumes these variables are defined:
%
% TestIn - input time series.
% TestOut - feedback time series.
clear;
load TestIn.csv;
load TestOut.csv;
X = tonndata(TestIn,false,false);
T = tonndata(TestOut,false,false);
% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation.
% 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
% The function PREPARETS prepares timeseries data for a particular network,
% shifting time by the minimum amount to fill input states and layer
% states. Using PREPARETS allows you to keep your original time series data
% unchanged, while easily customizing it for networks with differing
% numbers of delays, with open loop or closed loop feedback modes.
[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, ploterrhist(e)
%figure, plotregression(t,y)
%figure, plotresponse(t,y)
%figure, ploterrcorr(e)
%figure, plotinerrcorr(x,e)
% Closed Loop Network
% Use this network to do multi-step prediction.
% The function CLOSELOOP replaces the feedback input with a direct
% connection from the outout layer.
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(net,tc,yc);
% Step-Ahead Prediction Network
% For some applications it helps to get the prediction a timestep early.
% The original network returns predicted y(t+1) at the same time it is
% given y(t+1). For some applications such as decision making, it would
% help to have predicted y(t+1) once y(t) is available, but before the
% actual y(t+1) occurs. The network can be made to return its output a
% timestep early by removing one delay so that its minimal tap delay is now
% 0 instead of 1. The new network returns the same outputs as the original
% network, but outputs are shifted left one timestep.
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);
please help me , i really need your help. what code to get output as i describe before and write it to external output csv file ?
thank you.

  0 Comments

Sign in to comment.

1 Answer

Answer by Staffan
on 12 May 2016

Dear Bridget,
Have you only used Matlab for two days? Impressive!
I am also working with predictions using narxnet and narnet, I can't solve your question completely but I would like to advice you to have a look at the following newsgroup threads:
Wish you the best, (if you have the time,) please share your progress. I have a few of my own questions in progress, this one might be interesting for you to follow:
Regards
Staffan

  1 Comment

Staffan
on 12 May 2016
When you have created the vector with the predicted values, just use
...to write to file.
Regards
Staffan

Sign in to comment.