Neural network inputs and Outputs with different time delay
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It is possible to configure and train a NN with diffrent time delays in the MATLAB neural network tool box such as:
Y(t)=F(x(t),x(t-4),x(t-7),y(t-1),y(t-3)) with F the model.
or
Y(t)=F(x1(t-1),x2(t-2),x4(t-4))
Unfortunately NARX work only with same time delay for all inputs and outputs
Thanks
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채택된 답변
Greg Heath
2013년 10월 8일
Incorrect.
The number and values of ID and FD are independent.
Simple example:
close all, clear all, clc, plt=0
[X,T] = simplenarx_dataset;
net = narxnet(1,1:2,10);
view(net)
[Xs,Xi,Ai,Ts] = preparets(net,X,{},T);
whos
rng(0)
[net tr Ys Es Xf Yf] = train(net,Xs,Ts,Xi,Ai);
view(net)
whos
ts = cell2mat(Ts);
MSE00 = var(ts,1) % 0.099154
ys = cell2mat(Ys);
es = cell2mat(Es);
R2 = 1-mse(es)/MSE00 % 1
plt=plt+1,figure(plt)
hold on
plot(ts,'o','LineWidth',2)
plot(ys,'r--','LineWidth',2)
Hope this helps.
Thank you for formally accepting my answer
Greg
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Greg Heath
2013년 10월 13일
>Using auto/cross-correlation functions to determine the lags is useful for linear systems but for nonlinear systems with high complexity it does not work.
Bull.
Can you illustrate this with one of the many MATLAB timeseries datasets at
help nndatasets
Greg
Greg Heath
2013년 10월 13일
Very often using an underpowered model for input variable selection works surprisingly well.
추가 답변 (2개)
Greg Heath
2014년 10월 10일
I think the above discussions are somewhat confusing. My main points are
1. Use the target auto and target/input cross-correlation functions to determine the significant lags.
2. In a MIMO system the significant lags may be different for different inputs and input/target
combinations.
3. However,
a. all inputs must have the same nonnegative lags (which may be nonconsecutive)
b. All output feedback must have the same positive lags (which may be nonconsecutive)
Hope this helps,
Greg
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Greg Heath
2014년 10월 14일
No you misunderstood.
For a MIMO model all inputs must have the same set of input delays, e.g., ID = [ 1 3 6 ]. However, you might want to look at the significant lags for each crosscorrelation function of each input/output combination. Then choose a subset of those.
Correspondingly, for a MIMO model all targets must have the same set of feedback delays, e.g., FD = [ 2 6 ]. However, you might want to look at the significant lags for each target autocorrelation function of each target. Then choose a subset of those.
Yu-hsuan Lo
2018년 7월 9일
편집: Yu-hsuan Lo
2018년 7월 9일
Still now,
I can't ensure that the Input delay set like [1 3 6] is equal to the result :
y(t) = f{ x(t-1) , x(t-2) , x(t-3) }.
Because the data treated by 'preparets' is the same when the delay seeting is [1 3 6] , [ 6 ] or [3 6], and the data feed into my NN model(NARX) are the same, like
NARX = train(NARX,Xs,Ts,Xi).
So, will my NARX(below) work what I want : y(t) = f { y(t-1) , x(t-3) } ?
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/189832/image.png)
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