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Author Dreiseitl, S. ♦ Jacak, W.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1995
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Genetic algorithms ♦ Neural networks ♦ Modeling ♦ Nonlinear dynamical systems ♦ Artificial neural networks ♦ Network topology ♦ Nonlinear systems ♦ Neurons ♦ Encoding ♦ Computer networks
Abstract The modeling of nonlinear dynamical systems is one of the emergent application areas of artificial neural networks. In this paper, we present a general methodology based on neural networks and genetic algorithms that can be applied to modeling of nonlinear dynamical systems. We describe a general methodology for modeling nonlinear systems with known rank (i.e. state-space dimension) by feedforward networks with external delay units. We point out the shortcomings of this approach when the rank of the system is not known a priori. In this case, it is beneficial to employ genetic algorithms to search for neural networks that can model the nonlinear dynamical systems. Two genetic algorithms are presented for this case: one that determines the best feedforward network with external delay, and one that searches for a network with arbitrary topology and memory cells within each neuron.
Description Author affiliation: Res. Inst. for Symbolic Comput., Johannes Kepler Univ., Linz, Austria (Dreiseitl, S.; Jacak, W.)
ISBN 0780327594
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1995-11-29
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 551.72 kB
Page Count 6
Starting Page 602
Ending Page 607


Source: IEEE Xplore Digital Library