The problem of functional reconstruction of a
polynomial system from its noisy time-series
measurement is addressed in this paper. The
reconstruction requires the determination of the
embedding dimension and the unknown polynomial
structure. The authors propose the use of genetic
programming (GP) to find the exact functional form and
embedding dimension of an unknown polynomial system
from its time-series measurement. Using functional
operators of addition, multiplication and time delay,
they use GP to reconstruct the exact polynomial system
and its embedding dimension. The proposed GP approach
uses an improved least-squares (ILS) method to
determine the parameters of a polynomial system. The
ILS method is based on the orthogonal Euclidean
distance to obtain an accurate parameter estimate when
the series is corrupted by measurement noise.
Simulations show that the proposed ILS-GP method can
successfully reconstruct a polynomial system from its
noisy time-series measurements
%0 Journal Article
%1 varadan:2001:TIE
%A Varadan, Vinay
%A Leung, Henry
%D 2001
%J IEEE Transactions on Industrial Electronics
%K Euclidean accurate addition, algorithms, delay, dimension, distance, embedding estimate, functional genetic improved least-squares measurements, method, multiplication, noise, noisy operators, orthogonal parameter polynomial polynomials, programming, reconstruction, signal structure systems time time-series unknown
%N 4
%P 742--748
%R doi:10.1109/41.937405
%T Reconstruction of polynomial systems from noisy
time-series measurements using genetic programming
%V 48
%X The problem of functional reconstruction of a
polynomial system from its noisy time-series
measurement is addressed in this paper. The
reconstruction requires the determination of the
embedding dimension and the unknown polynomial
structure. The authors propose the use of genetic
programming (GP) to find the exact functional form and
embedding dimension of an unknown polynomial system
from its time-series measurement. Using functional
operators of addition, multiplication and time delay,
they use GP to reconstruct the exact polynomial system
and its embedding dimension. The proposed GP approach
uses an improved least-squares (ILS) method to
determine the parameters of a polynomial system. The
ILS method is based on the orthogonal Euclidean
distance to obtain an accurate parameter estimate when
the series is corrupted by measurement noise.
Simulations show that the proposed ILS-GP method can
successfully reconstruct a polynomial system from its
noisy time-series measurements
@article{varadan:2001:TIE,
abstract = {The problem of functional reconstruction of a
polynomial system from its noisy time-series
measurement is addressed in this paper. The
reconstruction requires the determination of the
embedding dimension and the unknown polynomial
structure. The authors propose the use of genetic
programming (GP) to find the exact functional form and
embedding dimension of an unknown polynomial system
from its time-series measurement. Using functional
operators of addition, multiplication and time delay,
they use GP to reconstruct the exact polynomial system
and its embedding dimension. The proposed GP approach
uses an improved least-squares (ILS) method to
determine the parameters of a polynomial system. The
ILS method is based on the orthogonal Euclidean
distance to obtain an accurate parameter estimate when
the series is corrupted by measurement noise.
Simulations show that the proposed ILS-GP method can
successfully reconstruct a polynomial system from its
noisy time-series measurements},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Varadan, Vinay and Leung, Henry},
biburl = {https://www.bibsonomy.org/bibtex/253d7e50ee86f0ba42847cda75dc832c7/brazovayeye},
doi = {doi:10.1109/41.937405},
interhash = {54b6b6f617485acd79bf4bea97cb2c77},
intrahash = {53d7e50ee86f0ba42847cda75dc832c7},
issn = {0278-0046},
journal = {IEEE Transactions on Industrial Electronics},
keywords = {Euclidean accurate addition, algorithms, delay, dimension, distance, embedding estimate, functional genetic improved least-squares measurements, method, multiplication, noise, noisy operators, orthogonal parameter polynomial polynomials, programming, reconstruction, signal structure systems time time-series unknown},
month = {August},
notes = {CODEN: ITIED6 INSPEC Accession Number:7007126},
number = 4,
pages = {742--748},
size = {7 pages},
timestamp = {2008-06-19T17:53:36.000+0200},
title = {Reconstruction of polynomial systems from noisy
time-series measurements using genetic programming},
volume = 48,
year = 2001
}