Abstract
Recurrent neural networks are popular tools used for modeling time series. Common gradient-based algorithms are frequently used for training recurrent neural networks. On the other side approaches based on the Kalman filtration are considered to be the most appropriate general-purpose training algorithms with respect to the modeling accuracy. Their main drawbacks are high computational requirements and difficult implementation. In this work we first provide clear description of the training algorithm using simple pseudo-language. Problem with high computational requirements is addresses by performing calculation on Multicore Processor and CUDA-enabled graphic processor unit. We show that important execution time reduction can be achieved by performing computation on manycore graphic processor unit.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Werbos, P.: Backpropagation through time; what it does and how to do it. Proceedings of the IEEE 78, 1550–1560 (1990)
Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1, 270–280 (1989)
Williams, R.J.: Some observations on the use of the extended Kalman filter as a recurrent network learning algorithm. Technical Report NU-CCS-92-1, Northeastern University, College of Computer Science, Boston, MA (1992)
Čerňanský, M., Beňušková, Ľ.: Simple recurrent network trained by RTRL and extended Kalman filter algorithms. Neural Network World 13(3), 223–234 (2003)
Trebatický, P.: Recurrent neural network training with the kalman filter-based techniques. Neural network world 15(5), 471–488 (2005)
Feldkamp, L., Prokhorov, D., Eagen, C., Yuan, F.: Enhanced multi-stream Kalman filter training for recurrent networks. In: Suykens, J., Vandewalle, J. (eds.) Nonlinear Modeling: Advanced Black-Box Techniques, pp. 29–53. Kluwer Academic Publishers, Dordrecht (1998)
Prokhorov, D.V.: Toyota prius hev neurocontrol and diagnostics. Neural Networks 21, 458–465 (2008)
NVIDIA: NVIDIA CUDA programming guide. Technical report (2008)
Trebatický, P.: Neural network training with extended kalman filter using graphics processing unit. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part II. LNCS, vol. 5164, pp. 198–207. Springer, Heidelberg (2008)
Prokhorov, D.V.: Kalman filter training of neural networks: Methodology and applications. In: Tutorial on IJCNN 2004, Budapest, Hungary (2004)
Elman, J.L.: Finding structure in time. Cognitive Science 14, 179–211 (1990)
Elman, J.: Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning 7, 195–225 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Čerňanský, M. (2009). Training Recurrent Neural Network Using Multistream Extended Kalman Filter on Multicore Processor and Cuda Enabled Graphic Processor Unit. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-04274-4_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
Online ISBN: 978-3-642-04274-4
eBook Packages: Computer ScienceComputer Science (R0)