Neural Network Tool Box
Hello everybody
I am new to neural networks, but I have studied the theory and everything is OK. Now I’ve come to the practical part. I am using the Neural Network toolbox in Matlab, and start using NARX where x(t) is Excel file (1 column and 3500 rows) and y(t) is also an Excel file (1 column and 3500 rows). I import these files and train the system, and get the figures and everything is OK. My question is:
After doing all of the above , how do I use this network to predict the value of y for a given x?
Here is the resulting script file :
% Solve an Autoregression Problem with External Input with a NARX Neural Network
% Script generated by NTSTOOL
% Created Fri Oct 12 20:48:21 IST 2012
%
% This script assumes these variables are defined:
%
% EMA - input time series.
% Close - feedback time series.inputSeries = tonndata(EMA,false,false);
targetSeries = tonndata(Close,false,false);% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:2;
feedbackDelays = 1:2;
hiddenLayerSize = 10;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);% Choose Input and Feedback Pre/Post-Processing Functions
% Settings for feedback input are automatically applied to feedback output
% For a list of all processing functions type: help nnprocess
% Customize input parameters at: net.inputs{i}.processParam
% Customize output parameters at: net.outputs{i}.processParam
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'};% 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.
[inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);% Setup Division of Data for Training, Validation, Testing
% The function DIVIDERAND randomly assigns target values to training,
% validation and test sets during training.
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand'; % Divide data randomly
% The property DIVIDEMODE set to TIMESTEP means that targets are divided
% into training, validation and test sets according to timesteps.
% For a list of data division modes type: help nntype_data_division_mode
net.divideMode = 'value'; % Divide up every value
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;% Choose a Training Function
% For a list of all training functions type: help nntrain
% Customize training parameters at: net.trainParam
net.trainFcn = 'trainlm'; % Levenberg-Marquardt% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
% Customize performance parameters at: net.performParam
net.performFcn = 'mse'; % Mean squared error% Choose Plot Functions
% For a list of all plot functions type: help nnplot
% Customize plot parameters at: net.plotParam
net.plotFcns = {'plotperform','plottrainstate','plotresponse', ...
'ploterrcorr', 'plotinerrcorr'};% Train the Network
[net,tr] = train(net,inputs,targets,inputStates,layerStates);% Test the Network
outputs = net(inputs,inputStates,layerStates);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)% Recalculate Training, Validation and Test Performance
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)% View the Network
view(net)% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotregression(targets,outputs)
%figure, plotresponse(targets,outputs)
%figure, ploterrcorr(errors)
%figure, plotinerrcorr(inputs,errors)% 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,inputSeries,{},targetSeries);
yc = netc(xc,xic,aic);
closedLoopPerformance = perform(netc,tc,yc)% Early 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,inputSeries,{},targetSeries);
ys = nets(xs,xis,ais);
earlyPredictPerformance = perform(nets,ts,ys)
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% Neural Network Tool Box
% Asked by Bashar Ali on 12 Oct 2012 at 18:14
% Latest activity Edited by Image Analyst on 12 Oct 2012 at 19:30
% Hello everybody
% I am new to neural networks, but I have studied the theory and everything is
% OK. Now I've come to the practical part. I am using the Neural Network
% toolbox in Matlab, and start using NARX where x(t) is Excel file (1 column
% and 3500 rows) and y(t) is also an Excel file (1 column and 3500 rows). I
% import these files and train the system, and get the figures and everything
% is OK. My question is:
% % After doing all of the above , how do I use this network to predict the
% value of y for a given x?I think it is as simple as
y = net(x);
% Here is the resulting script file :
% Solve an Autoregression Problem with External Input with a NARX Neural
% Network Script generated by NTSTOOL
% Created Fri Oct 12 20:48:21 IST 2012
% This script assumes these variables are defined:
% EMA — input time series.
% Close — feedback time series.
close all, clear all, clc, plt = 0; % Input purge
>inputSeries = tonndata(EMA,false,false);
>targetSeries = tonndata(Close,false,false);
Not a good name. lower case close is a MATLAB function (see above).
I’ll just use X and T below
plt=plt+1;figure(plt)
plot(T)
plot(X)
% Create a Nonlinear Autoregressive Network with External Input
>inputDelays = 1:2;
Often need 0 for input (but not feedback)
>feedbackDelays = 1:2;
Use xcorr, crosscorr or a corrected version of nncorr (see below) too obtain the lags at significant peak values.
N = length(T);
ZT = zscore(T);
ZX = zscore(X);
autocorrT = nncorr(ZT,ZT,N-1);
crosscorrXTR = nncorr(ZX,ZT,N-1); % Incorrectly symmetric
crosscorrXTL = nncorr(ZT,ZX,N-1); % Incorrectly symmetric
crosscorrXT = [ crosscorrXTL(1:N-1), crosscorrXTR(N:end) ];
lags = -(N-1):N-1;
Enter code to find POSITIVE lags at significant peaks (Yeah, I could
have just used crosscorrXTR) but …, well, you understand)
>hiddenLayerSize = 10;
Choose optimal small value ( H <= (N-1)/3 ) by trial and error.
>net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
% Choose Input and Feedback Pre/Post-Processing Functions
% Settings for feedback input are automatically applied to feedback output
% For a list of all processing functions type: help nnprocess
% Customize input parameters at: net.inputs{i}.processParam
% Customize output parameters at: net.outputs{i}.processParam
>net.inputs{1}.processFcns = {‘removeconstantrows’,’mapminmax’};
>net.inputs{2}.processFcns = {‘removeconstantrows’,’mapminmax’};
Unnecessary. These are defaults
% Prepare the Data for Training and Simulation
% The function PREPARETS prepares timeseries data for a particular network,
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