Comments on "Backpropagation algorithms for a broad class of dynamic networks"

Authors:
Christian Endisch;Peter Stolze;Christoph Hackl;Dierk Schröder
Affiliations:
Institute for Electrical Drive Systems, Technical University of Munich, München, Germany;Institute for Electrical Drive Systems, Technical University of Munich, München, Germany;Institute for Electrical Drive Systems, Technical University of Munich, München, Germany;Institute for Electrical Drive Systems, Technical University of Munich, München, Germany
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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Abstract

In a recent paper, De Jesús et al. proposed a general framework for describing dynamic neural networks. Gradient and Jacobian calculations were discussed based on backpropagation-through-time (BPTT) algorithm and real-time recurrent learning (RTRL). Some errors in the paper of De Jesús et al. bring difficulties for other researchers who want to implement the algorithms. This comments paper shows the critical parts of the publication and gives errata to facilitate understanding and implementation.