[EDIT: 20110722 22:22 CDT – reformat – WDR]
Hi,
I used the GUI to create my NARX network, and the GUI allows me to perform additional tests on my network with new input and output, but I need to know how to write that in code. When I generate the simple script of the GUI, it doesn't show that code.
Thanks so much from your help. Below is the script that the GUI creates.
% Solve an Autoregression Problem with External Input with a NARX Neural Network
% Script generated by NTSTOOL
% Created Fri Jul 22 13:33:47 EDT 2011
%
% This script assumes these variables are defined:
%% p1 - input time series.
% p2 - feedback time series.
inputSeries = tonndata(p1,false,false);targetSeries = tonndata(p2,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, ploterrcorr(errors)
%figure, plotinerrcorr(inputs,errors)
Best Answer