Version 1
: Received: 20 April 2021 / Approved: 20 April 2021 / Online: 20 April 2021 (08:49:55 CEST)
How to cite:
Luttmann, L.; Mercorelli, P. Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks. Preprints2021, 2021040523. https://doi.org/10.20944/preprints202104.0523.v1
Luttmann, L.; Mercorelli, P. Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks. Preprints 2021, 2021040523. https://doi.org/10.20944/preprints202104.0523.v1
Luttmann, L.; Mercorelli, P. Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks. Preprints2021, 2021040523. https://doi.org/10.20944/preprints202104.0523.v1
APA Style
Luttmann, L., & Mercorelli, P. (2021). Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks. Preprints. https://doi.org/10.20944/preprints202104.0523.v1
Chicago/Turabian Style
Luttmann, L. and Paolo Mercorelli. 2021 "Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks" Preprints. https://doi.org/10.20944/preprints202104.0523.v1
Abstract
This work describes and compares the backpropagation algorithm with the Extended Kalman filter, a second-order training method which can be applied to the problem of learning neural network parameters and is known to converge in only a few iterations. The algorithms are compared with respect to their effectiveness and speed of convergence using simulated data for both, a regression and a classification task.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
