title: Comments on "Backpropagation algorithms for a broad class of dynamic networks".
abstract: In a recent paper, De Jesús 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 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.
authors.affiliation: Institute for Electrical Drive Systems, Technical University of Munich, 80333 München, Germany.christian.endisch@tum.de
authors.forename: Christian
authors.initials: C
authors.lastname: Endisch
authors.forename: Peter
authors.initials: P
authors.lastname: Stolze
authors.forename: Christoph
authors.initials: C
authors.lastname: Hackl
authors.forename: Dierk
authors.initials: D
authors.lastname: Schroder
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer