Nonlinear System Identification: An Overview

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Nonlinear System Identification: An Overview

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dc.contributor.author Zoubir, A.M.
dc.contributor.author Boashash, B
dc.date.accessioned 2012-02-15T09:54:52Z
dc.date.available 2012-02-15T09:54:52Z
dc.date.issued 1993-10
dc.identifier.citation The Arabian Journal for Science and Engineering, Vol. 18, No. 4, pp. 423-458 en_US
dc.identifier.uri http://hdl.handle.net/10576/10785
dc.description This paper presents an introductory overview of non-linear system identification. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated). en_US
dc.description.abstract System identification consists in the characterization of a system from an analysis of observed input and output signals. In essence , the ultimate aim of system identification is prediction such that given a description of the system transfer parameters and the input, . the output can be completely specified for any time. This paper reviews traditional and contemporary methods used for non-linear system identification. Their motivations and justifications to characterize their properties are discussed for several classes of non-linear systems. Comparisons to conventional linear approaches are made. Parametric and non-parametric models, excited by both deterministic and stochastic signals are presented . Many contributions in the area of non-linear systems identification make the simplifying assumption that the input excitation is white and Gaussian. This document discusses issues related to non-linear transformations of non-Gaussian data, and gives examples of real-life situations where linearity and Gaussianity assumptions are not valid. en_US
dc.language.iso en en_US
dc.publisher The Arabian Journal for Science and Engineering en_US
dc.subject Non-linear System Identification en_US
dc.subject prediction en_US
dc.subject coherence function en_US
dc.subject phase-locked loop en_US
dc.title Nonlinear System Identification: An Overview en_US
dc.type Article en_US

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