NDLI: Multi-step-ahead prediction using dynamic recurrent neural networks

Content Provider	IEEE Xplore Digital Library
Author	Parlos, A.G. Rais, O.T. Atiya, A.F.
Copyright Year	1999
Description	Author affiliation: Dept. of Nucl. Eng., Texas A&M Univ., College Station, TX, USA (Parlos, A.G.)
Abstract	In numerous problems, such as in process control utilizing predictive control algorithms, it is required that a variable of interest be predicted multiple time-steps ahead into the future without having measurements of that variable in the horizon of interest. Additionally, in applications involving forecasting and fault diagnosis the availability of multistep-ahead predictors (MSP) is desired. MSPs are difficult to design because lack of measurements in the prediction horizon necessitates the recursive use of single-step-ahead predictors for reaching the final point in the horizon. Even small prediction errors resulting from noise at each point in the horizon accumulate and propagate, often resulting in poor prediction accuracy. We present a method for designing MSP using dynamic recurrent neural networks. The method is based on a dynamic gradient descent learning algorithm and its effectiveness is demonstrated through applications to an open-loop unstable process system, namely a heat-exchanger.
Starting Page	349
Ending Page	352
File Size	367081
Page Count	4
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.831517
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Predictive models Algorithm design and analysis Finite impulse response filter IIR filters Process control Prediction algorithms Accuracy Neural networks Delay
Content Type	Text
Resource Type	Article

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