Comments on "Backpropagation Algorithms for a Broad Class of Dynamic Networks
Christian Endisch, Peter Stolze, Christoph M. Hackl, Dierk F. Schroder · IEEE Transactions on Neural Networks · 2009
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.