Was sind die Eingänge, Ausgänge und Ziel in ANN

Ich bin immer verwirrend zu Eingaben von Daten festlegen, Ergebnisse und Ziel. Ich studiere über Künstliche Neuronale Netz in Matlab, mein Bereich ist, wollte ich die Verwendung der history-Daten (ich habe die Niederschläge und Wasserstände seit 20 Jahren) voraussagen, Wasser-Niveau in der Zukunft (z.B. 2014). So, wo ist meine Eingaben, Ziele und output? Zum Beispiel habe ich ein Excel-sheet Daten als [Column1-Datum| Column2-Niederschlag | Column3 - |Wasser-level]

Ich bin mit diesem code zur Vorhersage, aber es konnte nicht Vorhersagen, in der Zukunft, kann mir jemand helfen, es wieder zu beheben? Danke .

%% 1. Importing data
Data_Inputs=xlsread('demo.xls'); % Import file

Training_Set=Data_Inputs(1:end,2);%specific training set

Target_Set=Data_Inputs(1:end,3); %specific target set

Input=Training_Set'; %Convert to row

Target=Target_Set'; %Convert to row

X = con2seq(Input); %Convert to cell

T = con2seq(Target); %Convert to cell

%% 2. Data preparation

N = 365; % 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);

inputSeriesVal  = X(end-N+1:end);

targetSeriesVal = T(end-N+1:end); 
% Create a Nonlinear Autoregressive Network with External Input
delay = 2;
inputDelays = 1:2;
feedbackDelays = 1:2;
hiddenLayerSize = 10;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);

% 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
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% 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)

% 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)

%% 5. Multi-step ahead prediction

inputSeriesPred  = [inputSeries(end-delay+1:end),inputSeriesVal];

targetSeriesPred = [targetSeries(end-delay+1:end), con2seq(nan(1,N))];

[Xs,Xi,Ai,Ts] = preparets(netc,inputSeriesPred,{},targetSeriesPred);

yPred = netc(Xs,Xi,Ai);

perf = perform(net,yPred,targetSeriesVal);

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');