NDLI: Nonlinear System Identification: An Effective Framework Based on the Karhunen

Content Provider	IEEE Xplore Digital Library
Author	Turchetti, C. Biagetti, G. Gianfelici, F. Crippa, P.
Copyright Year	1991
Abstract	This paper proposes, on the basis of a rigorous mathematical formulation, a general framework that is able to define a large class of nonlinear system identifiers. This framework exploits all those relationships that intrinsically characterize a limited set of realizations, obtained by an ensemble of output signals and their parameterized inputs, by means of the separation property of the Karhunen-Loeve transform. The generality and the flexibility of the approximating mappings (ranging from traditional approximation techniques to multiresolution decompositions and neural networks) allow the design of a large number of distinct identifiers each displaying a number of properties such as linearity with respect to the parameters, noise rejection, low computational complexity of the approximation procedure. Exhaustive experimentation on specific case studies reports high identification performance for four distinct identifiers based on polynomials, splines, wavelets and radial basis functions. Several comparisons show how these identifiers almost always have higher performance than that obtained by current best practices, as well as very good accuracy, optimal noise rejection, and fast algorithmic elaboration. As an example of a real application, the identification of a voice communication channel, comprising a digital enhanced cordless telecommunications (DECT) cordless phone for wireless communications and a telephone line, is reported and discussed.
Sponsorship	IEEE Signal Processing Society
Starting Page	536
Ending Page	550
Page Count	15
File Size	1615041
File Format	PDF
ISSN	1053587X
Volume Number	57
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear systems Karhunen-Loeve transforms Signal resolution Neural networks Linearity Computational complexity Polynomials Best practices Communication channels Wireless communication wavelet approximation Hilbert space Karhunen–LoÈve transform (KLT) nonlinear approximation nonlinear mapping nonlinear system identification radial basis functions (RBF) statistical signal processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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