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Title:
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Nonlinear System Identification: An Overview |
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Author:
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Zoubir, A.M.; Boashash, B
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Abstract:
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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. |
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Description:
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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). |
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URI:
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http://hdl.handle.net/10576/10785
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Date:
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1993-10 |