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Modeling of nonlinear dynamic systems via discrete-time recurrent neural networks and variational training algorithm

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2 Author(s)
S. V. Minchev ; Fac. of Appl. Math. & Informatics, Tech. Univ. of Sofia, Bulgaria ; G. I. Venkov

This paper proposes a discrete-time recurrent neural network architecture and parameter adaptation algorithm for modeling of nonlinear dynamic systems. The learning algorithm is based on variational calculus and operates off-line. A neural network based current transformer nonlinear model is presented as a demonstration of the proposed architecture and learning algorithm. It is designed for power engineering needs in power systems and is suited for real-time applications in digital relay protections.

Published in:

Intelligent Systems, 2004. Proceedings. 2004 2nd International IEEE Conference  (Volume:1 )

Date of Conference:

22-24 June 2004