Frequency domain identification of Wiener models

Authors:
Er-Wei Bai
Affiliations:
Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA
Venue:
Automatica (Journal of IFAC)
Year:
2003

Citing 6
Cited 13

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Parameter bounds evaluation of Wiener models with noninvertible polynomial nonlinearities

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Hammerstein uniform cubic spline adaptive filters: Learning and convergence properties

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Abstract

This paper proposes a frequency domain algorithm for Wiener model identifications based on exploring the fundamental frequency and harmonics generated by the unknown nonlinearity. The convergence of the algorithm is established in the presence of white noise. No a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric.