NDLI: A Novel Fast Orthogonal Search Method for design of functional link networks and their use in system identification

Content Provider	IEEE Xplore Digital Library
Author	Abbas, H.M.
Copyright Year	2007
Description	Author affiliation: Mentor Graphics Egypt, Cairo (Abbas, H.M.)
Abstract	In this paper, a functional link neural net (FLN) capable of performing sparse nonlinear system identification is proposed. Korenberg's fast orthogonal search (FOS) is adopted to detect the proper model and its associated parameters. The FOS algorithm is modified by first sorting all possible nonlinear functional expansion of the input pattern according to their correlation with the system output. The sorted functions are divided into equal size groups, pins, where functions with the highest correlation with the output are assigned to the first pin. Lower correlation members go the following pin and so forth. During the identification process, members in lower pins are tried first. If a solution is not found, next pins join the candidates pool until the identification process completes within prespecified accuracy. The modified Gram Schmidt orthogonalization and Choleskey decomposition are applied to create orthogonal functionals that can linearly fit the identified system. The proposed architecture is tested on noise-free and noisy nonlinear systems and shown to find sparse models that can approximate the experimented systems with acceptable accuracy.
Starting Page	2743
Ending Page	2747
File Size	132943
Page Count	5
File Format	PDF
ISBN	9781424409907
DOI	10.1109/ICSMC.2007.4414037
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Search methods Design methodology System identification Nonlinear systems Polynomials Pins Neural networks Noise cancellation Parameter estimation Evolutionary computation
Content Type	Text
Resource Type	Article

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