Plotting simplenarx_dataset

function [inputs,targets] = simplenarx_dataset %SIMPLENARX_DATASET Simple time-series prediction dataset % % Input-output time series problems consist of predicting the next value % of one time-series given another time-series. Past values of both series % (for best accuracy), or only one of the series (for a simpler system) % may be used to predict the target series. % % This dataset can be used to demonstrate how a neural network can be % trained to make predictions. % % LOAD simplenarx_dataset.MAT loads these two variables: % % simplenarxInputs - a 1x100 cell array of scalar values representing % a 100 timestep time-series. % % simplenarxTargets - a 1x100 cell array of scalar values representing % a 100 timestep time-series to be predicted. % % [X,T] = simplenarx_dataset loads the inputs and targets into % variables of your own choosing. % % You can import this example data set from within the Neural Net Time % Series app. Open the app by calling ntstool. % % Here is how to design a neural network that predicts the target series % from past values of inputs and targets. See narxnet for more details. % % [X,T] = simplenarx_dataset; % net = narxnet(1:2,1:2,10); % [Xs,Xi,Ai,Ts] = preparets(net,X,{},T); % net = train(net,Xs,Ts,Xi,Ai); % view(net) % Y = net(Xs,Xi,Ai) % plotresponse(Ts,Y) % % Here is how to design a neural network that predicts the target series % from only using past values of inputs. See timedelaynet for details. % % net = timedelaynet(1:2,10); % [Xs,Xi,Ai,Ts] = preparets(net,X,T); % net = train(net,Xs,Ts,Xi,Ai); % % Here is how to design a neural network that predicts the targets series % only using past values of the target series. See narnet for details. % % net = narnet(1:2,10); % [Xs,Xi,Ai,Ts] = preparets(net,{},{},T); % net = train(net,X,T,Xi,Ai); % % See also NTSTOOL, NARXNET, TIMEDELAYNET, NARNET, PREPARETS, NNDATASETS. % Copyright 2010-2021 The MathWorks, Inc. load simplenarx_dataset inputs = simplenarxInputs; targets = simplenarxTargets;