Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks

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. Preprints 2021, 2021040523 (doi: 10.20944/preprints202104.0523.v1). Luttmann, L.; Mercorelli, P. Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks. Preprints 2021, 2021040523 (doi: 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.

Subject Areas

Backpropagation Algorithm; Kalman Filter; Neural Networks

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