Title :
Comments on "Backpropagation Algorithms for a Broad Class of Dynamic Networks
Author :
Endisch, Christian ; Stolze, Peter ; Hackl, Christoph ; Schröder, Dierk
Author_Institution :
Inst. for Electr. Drive Syst., Tech. Univ. of Munich, Munich
fDate :
3/1/2009 12:00:00 AM
Abstract :
"For original paper see O. De Jesus, .ibid., vol. 18, no. 1, p.14-27,(2007)",. In a recent paper, De Jesus 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 Jesus 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.
Keywords :
Jacobian matrices; backpropagation; gradient methods; recurrent neural nets; Jacobian calculation; backpropagation-through-time algorithm; dynamic neural network; gradient calculation; real-time recurrent learning; recurrent neural network; Backpropagation algorithms; Computer languages; Equations; Jacobian matrices; Neural networks; Neurons; Recurrent neural networks; Sections; System identification; Backpropagation-through-time (BPTT); dynamic neural network; layered digital dynamic network (LDDN); real-time recurrent learning (RTRL); recurrent neural network; Algorithms; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2009.2013243
