Multi-step-ahead prediction using dynamic recurrent neural networks

• Published in: IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Vol. 1; pp. 349 - 352 vol.1
• Main Authors: Parlos, A.G., Rais, O.T., Atiya, A.F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1999
• Subjects: Accuracy; Algorithm design and analysis; Delay; Finite impulse response filter; IIR filters; Neural networks; Prediction algorithms; Predictive models; Process control; Recurrent neural networks
• ISBN: 0780355296; 9780780355293
• ISSN: 1098-7576; 1558-3902
• DOI: 10.1109/IJCNN.1999.831517

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.