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Adaptive estimation of third‐order frequency domain Volterra kernels
Author(s) -
Tseng ChingHsiang,
Powers Edward J.
Publication year - 1996
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/(sici)1099-1115(199603)10:2/3<319::aid-acs353>3.0.co;2-s
Subject(s) - frequency domain , computation , gaussian , computer science , recursive least squares filter , algorithm , simple (philosophy) , adaptive algorithm , domain (mathematical analysis) , adaptive filter , control theory (sociology) , kernel (algebra) , mathematics , artificial intelligence , mathematical analysis , physics , control (management) , quantum mechanics , philosophy , epistemology , computer vision , combinatorics
Abstract Techniques for adaptive estimation of frequency domain Volterra kernels of non‐linear systems up to the third order are investigated. A simple adaptive algorithm for identifying the frequency domain Volterra kernels is derived based on the assumption that the input is Gaussian. Compared with the conventional adaptive method based on the recursive least squares (RLS) technique, the proposed method requires substantially less computation and computer memory. The insensitivity of the proposed method to the Gaussianity assumption of the input is also demonstrated via computer simulation and application to experimental data. The results show that the proposed adaptive method is comparable in performance with the conventional RLS‐based adaptive method even when the input is non‐Gaussian.