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The following TR is available from the NEC Research Institute and
University of Maryland UMIACS archives.

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 A Delay Damage Model Selection Algorithm for NARX Neural Networks


Tsungnan Lin{1,2}, C. Lee Giles{1,3}, Bill G. Horne{1}, Sun-Yang Kung{2} 

{1} NEC Research Institute, 4 Independence Way, Princeton, NJ 08540
{2} Dept. of Electrical Engineering, Princeton U., Princeton, NJ 08540
{3} UMIACS, University of Maryland, College Park, MD 20742 

    U. of Maryland Technical Report CS-TR-3707 and UMIACS-TR-96-77


                            ABSTRACT

Recurrent neural networks have become popular models for system
identification and time series prediction. NARX (Nonlinear 
AutoRegressive models with eXogenous inputs) neural network models 
are a popular subclass of recurrent networks and have beenused in 
many applications. Though embedded memory can be found in all recurrent 
network models, it is particularly prominent in NARX models.

We show that using intelligent memory order selection through pruning 
and good initial heuristics significantly improves the generalization 
and predictive performance of these nonlinear systems on problems as 
diverse as grammatical inference and time series prediction.  


Keywords: Recurrent neural networks, tapped-delay lines,
long-term dependencies, time series, automata, memory, temporal
sequences, gradient descent training, latching, NARX networks,
auto-regressive, pruning, embedding theory.

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http://www.neci.nj.nec.com/homepages/giles.html
http://www.cs.umd.edu/TRs/TR-no-abs.html 

--                                 
C. Lee Giles / Computer Sciences / NEC Research Institute / 
4 Independence Way / Princeton, NJ 08540, USA / 609-951-2642 / Fax 2482
www.neci.nj.nec.com/homepages/giles.html
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