Abstract
The aim of this paper is to demonstrate the suitability of recurrent neural networks (RNN) for state estimation and tracking problems that are traditionally solved using Kalman Filters (KF). This paper details a simulation study in which the performance of a basic discrete time KF is compared with that of an equivalent neural filter built using an RNN. Real time recurrent learning (RTRL) algorithm is used to train the RNN. The neural network is found to provide comparable performance to that of the KF in both the state estimation and tracking problems. The relative merits and demerits of KF vs RNN are discussed with respect to computational complexity, ease of training and real time issues.
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References
Brookner, E.: Tracking and kalman filtering made easy. John Wiley and Sons, Inc, New York (1998)
Pillai, S.K., Seshagiri Rao, V., Balakrishanan, S.S., Sankar, S.: A New Method of Model Compensation in Kalman Filter - Application to Launch Vehicles. In: Proceedings of The Fourteenth International Symposium on Space Technology and Science, Tokyo (1984)
Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1, 270–280 (1989)
Haykin, S.: Neural Networks, 2nd edn. Pearson Eductation, Inc, New Delhi (1999)
Werbos, P.: J. Backpropagation Through Time: What it does and how to do it? In: Proc. of the ICNN, SanFrancisco, CA, USA (1993)
DeCruyenaere, J.P., Hafez, H.M.: A comparison between Kalman filters and recurrent neural networks. In: Proc. of the IJCNN, Baltimore, USA, vol. IV, pp. 247–251. IEEE Press, Los Alamitos (1992)
Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1(I), 4–27 (1990)
Puskorius, G.V., Feldkamp, L.A.: Neurocontrol of Nonlinear Dynamical Systems with Kalman Filter Trained Recrrent Networks. IEEE Transactions on Neural Networks 5(2), 279–297 (1994)
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© 2004 Springer-Verlag Berlin Heidelberg
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Chenna, S.K. et al. (2004). State Estimation and Tracking Problems: A Comparison Between Kalman Filter and Recurrent Neural Networks. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_41
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DOI: https://doi.org/10.1007/978-3-540-30499-9_41
Publisher Name: Springer, Berlin, Heidelberg
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