An evolutionary program for the identification of
dynamical systems
P. Angeline, and D. Fogel. Application and Science of Artificial Neural Networks
III, 3077, page 409--417. (1997)
Abstract
Various forms of neural networks have been applied to
the identification of non-linear dynamical systems. In
most of these methods, the network architecture is set
prior to training. In this paper, a method that evolves
a symbolic solution for plant models is described. This
method uses an evolutionary program to manipulate
collections of parse trees expressed in a task specific
language. Experiments performed on two unknown plants
show this method is competitive with those that train
neural networks for similar problems
%0 Conference Paper
%1 angeline:1997:spie
%A Angeline, Peter J.
%A Fogel, David B.
%B Application and Science of Artificial Neural Networks
III
%D 1997
%E Rogers, S.
%K algorithms, computation, dynamical evolutionary genetic identification, optimization programming, system systems,
%P 409--417
%T An evolutionary program for the identification of
dynamical systems
%U http://www.natural-selection.com/Library/1997/spie97.pdf
%V 3077
%X Various forms of neural networks have been applied to
the identification of non-linear dynamical systems. In
most of these methods, the network architecture is set
prior to training. In this paper, a method that evolves
a symbolic solution for plant models is described. This
method uses an evolutionary program to manipulate
collections of parse trees expressed in a task specific
language. Experiments performed on two unknown plants
show this method is competitive with those that train
neural networks for similar problems
@inproceedings{angeline:1997:spie,
abstract = {Various forms of neural networks have been applied to
the identification of non-linear dynamical systems. In
most of these methods, the network architecture is set
prior to training. In this paper, a method that evolves
a symbolic solution for plant models is described. This
method uses an evolutionary program to manipulate
collections of parse trees expressed in a task specific
language. Experiments performed on two unknown plants
show this method is competitive with those that train
neural networks for similar problems},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Angeline, Peter J. and Fogel, David B.},
biburl = {https://www.bibsonomy.org/bibtex/2dc17abdf5165479f81ed7dcfb9aeaa01/brazovayeye},
booktitle = {Application and Science of Artificial Neural Networks
III},
editor = {Rogers, S.},
interhash = {e99f89d78a2018d699b402ce3aed32a3},
intrahash = {dc17abdf5165479f81ed7dcfb9aeaa01},
keywords = {algorithms, computation, dynamical evolutionary genetic identification, optimization programming, system systems,},
organisation = {SPIE-The International Society for Optical
Engineering},
pages = {409--417},
publisher_address = {Bellingham, WA, USA},
size = {9 pages},
timestamp = {2008-06-19T17:35:45.000+0200},
title = {An evolutionary program for the identification of
dynamical systems},
url = {http://www.natural-selection.com/Library/1997/spie97.pdf},
volume = 3077,
year = 1997
}