Backpropagation Algorithms for a Broad Class of Dynamic Networks

This paper introduces a general framework for describing dynamic neural networks-the layered digital dynamic network (LDDN). This framework allows the development of two general algorithms for computing the gradients and Jacobians for these dynamic networks: backpropagation-through-time (BPTT) and r...

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Bibliographic Details
Published in:	IEEE transactions on neural networks Vol. 18; no. 1; pp. 14 - 27
Main Authors:	Orlando De Jesus, Hagan, M.T.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.01.2007 Institute of Electrical and Electronics Engineers
Subjects:	Algorithms Applied sciences Artificial Intelligence Backpropagation algorithms Backpropagation through time (BPTT) Cluster Analysis Computer networks Computer science; control theory; systems Computer systems and distributed systems. User interface Computing Methodologies Connectionism. Neural networks Delay lines Digital dynamic neural networks Dynamics Exact sciences and technology gradient Heuristic algorithms Jacobian Jacobian matrices Jacobians layered digital dynamic network (LDDN) Learning Mathematical analysis Networks Neural networks Neural Networks (Computer) Neurofeedback Output feedback Pattern Recognition, Automated - methods Power engineering and energy real-time recurrent learning (RTRL) Recurrent neural networks Signal Processing, Computer-Assisted Software Backpropagation Recurrence Recurrent neural nets Gradient dynamic neural networks Neural network Distributed system Jacobi matrix Real time Search algorithm Backpropagation algorithm Backpropagation through time (BPTT) real-time recurrent learning (RTRL) recurrent neural networks Jacobian layered digital dynamic network (LDDN)
ISSN:	1045-9227
Online Access:	Get full text
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