How can i make a closed loop NarxNet with feedback of predicted values?

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Hi, i´m trying to make a closed-loop Narxnet feedbacking future values. I mean, with feedback of (y+1) or (y+5), but i can´t find a way to do it. I supposed that using the feedback delay with a positive number i could manage it, but i´m not sure if this is correct: net = narxnet(0,5,20); %20 neurons, 0 delay input, y+5 feedback? Instead of delaying it with 1:d
Thank you beforehand

采纳的回答

Greg Heath
Greg Heath 2016-1-22
"but once predicted, feedback them"
That is exactly what narxnet does. The default values ID,FD = (1:2,1:2) yield
1. y(t) = f( x(t-1), x(t-2), y(t-1), y(t-2)) for t >= 3
which can also be interpreted as
2. y(t+3) = f( x(t+2), x(t+1), y(t+2), y(t+1)) for t>= 0
The nondefault input ID = 0:2 adds x(t) to the RHS of 1
The nondefault input FD = 0:2 adds y(t) to the RHS of 1 for OPENLOOP ONLY!!!
FD = 0 is not allowed for CLOSELOOP. Any attempt to do so will result in either an ERROR, or the command is ignored.
Hope this helps.
Thank you for formally accepting my answer
Greg

更多回答(2 个)

Greg Heath
Greg Heath 2016-1-17
Read
help narxnet
doc narxnet
See posted examples in both the NEWSGROUP and ANSWERS
greg narxnet
greg narxnet tutorial
Reading the latest ones first may help.
  1 个评论
Luis Ignacio Ruiz
Luis Ignacio Ruiz 2016-1-17
I´ve already been looking for answers in the matlab help and forums. I just found informationa about multi-step ahead prediction, but what i really need to know is if there´s some way I can feeback those predictions before appearing. I mean, if the output is giving y(t), I would like to know if there´s a method to feeback y(t+1), y(t+2), y(t+3)... just like the normal feedback of previous values (y(t-1),y(t-2),..) but with the future ones.
Thank you beforehand

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Greg Heath
Greg Heath 2016-1-22
If you find a way to feedback signals before they are created,
CONTACT ME IMMEDIATELY!!!
I'LL MAKE US BOTH VERY, VERY, WEALTHY!!!
Greg
  1 个评论
Luis Ignacio Ruiz
Luis Ignacio Ruiz 2016-1-22
Haha, not before they´re created but once predicted, feeback them. I thought that the solution might be in feebacking the predicted values once we have the outputs "Y" in a new net, but I don´t know if it could be possible.
P.D.: Sorry for my lack of knowlegde about this subject. It´s the first year I´m studying neural networks, but it has a lot of potential and it´s a big part of my master´s degree final project so I´m looking as much as i can into it.

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