Identification of a class of nonlinear systems under stationary non-Gaussian excitation

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Identification of a class of nonlinear systems under stationary non-Gaussian excitation

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dc.contributor.author Ralston, J.C.
dc.contributor.author Zoubir, A.M.
dc.contributor.author Boashash, B
dc.date.accessioned 2011-07-26T04:53:13Z
dc.date.available 2011-07-26T04:53:13Z
dc.date.issued 1997-03
dc.identifier.citation IEEE Transactions on Signal Processing, Volume : 45 , Issue:3, page(s): 719 en_US
dc.identifier.issn 1053-587X
dc.identifier.uri http://hdl.handle.net/10576/10720
dc.description This paper presents a method for non-linear system identification using a non-gaussian input. (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 This paper provides new solutions to the nonlinear system identification problem when the input to the system is a stationary non-Gaussian process. We propose the use of a model called the Hammerstein series, which leads to significant reductions in both the computational requirements and the mathematical tractability of the nonlinear system identification problem. We show that unlike the Volterra series, one can obtain closed-form expressions for the Hammerstein series kernels and the quadratic coherence function in the non-Gaussian case. Estimation of the kernels and quadratic coherence function is discussed. A comparison with a nonlinear system identification approach that uses the Volterra series is provided. An automotive engineering application illustrates the usefulness of the proposed method. en_US
dc.description.sponsorship IEEE Signal Processing Society en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Hammerstein series en_US
dc.subject automotive engineering application en_US
dc.subject closed-form expressions en_US
dc.subject computational requirements en_US
dc.subject identification en_US
dc.subject kernels en_US
dc.subject mathematical tractability en_US
dc.subject nonlinear systems en_US
dc.subject quadratic coherence function en_US
dc.subject stationary non-Gaussian excitation en_US
dc.subject non-linear system identification en_US
dc.title Identification of a class of nonlinear systems under stationary non-Gaussian excitation en_US
dc.type Article en_US

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