NDLI: Reconstruction of polynomial systems from noisy time-series measurements using genetic programming

Content Provider	IEEE Xplore Digital Library
Author	Varadan, V. Leung, H.
Copyright Year	1982
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.
Sponsorship	IEEE Industrial Electronics Society
Starting Page	742
Ending Page	748
Page Count	7
File Size	178564
File Format	PDF
ISSN	02780046
Volume Number	48
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Polynomials Genetic programming Parameter estimation Noise measurement Nonlinear dynamical systems Least squares approximation Time measurement Chaos Delay effects Euclidean distance
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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