S = load('magdata'); X = con2seq(S.u); T = con2seq(S.y); %% 2. Data preparation N = 300; % Multi-step ahead prediction % Input and target series are divided in two groups of data: % 1st group: used to train the network inputSeries = X(1:end-N); targetSeries = T(1:end-N); % 2nd group: this is the new data used for simulation. inputSeriesVal will % be used for predicting new targets. targetSeriesVal will be used for % network validation after prediction inputSeriesVal = X(end-N+1:end); targetSeriesVal = T(end-N+1:end); % This is generally not available %% 3. Network Architecture delay = 2; neuronsHiddenLayer = 10; % Network Creation net = narxnet(1:delay,1:delay,neuronsHiddenLayer); %% 4. Training the network [Xs,Xi,Ai,Ts] = preparets(net,inputSeries,{},targetSeries); net = train(net,Xs,Ts,Xi,Ai); view(net) Y = net(Xs,Xi,Ai); % Performance for the series-parallel implementation, only % one-step-ahead prediction perf = perform(net,Ts,Y); %% 5. Multi-step ahead prediction [Xs1,Xio,Aio] = preparets(net,inputSeries(1:end-delay),{},targetSeries(1:end-delay)); [Y1,Xfo,Afo] = net(Xs1,Xio,Aio); [netc,Xic,Aic] = closeloop(net,Xfo,Afo); [yPred,Xfc,Afc] = netc(inputSeriesVal,Xic,Aic); multiStepPerformance = perform(net,yPred,targetSeriesVal); view(netc) figure; plot([cell2mat(targetSeries),nan(1,N); nan(1,length(targetSeries)),cell2mat(yPred); nan(1,length(targetSeries)),cell2mat(targetSeriesVal)]') legend('Original Targets','Network Predictions','Expected Outputs')
