Nonlinear system identification using quasi-perfect periodic sequences

Sicuranza, Giovanni L.; Carini, Alberto

doi:10.1016/j.sigpro.2015.08.018

Cited by 11 publications

(7 citation statements)

References 51 publications

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Order By: Relevance

“…where f : R N 1 → R is a continuous function [9]. By definition, the order of a FLiP basis function is the sum of the orders of its factors g i [ξ].…”

Section: Flip Filtersmentioning

confidence: 99%

“…Nonlinear adaptive filters are frequently used to identify unknown nonlinear systems. Very often the adaptive filter belongs to the class of functional link polynomial (FLiP) filters [9], [10]. This is a broad class of linear-in-the-parameters (LIP) nonlinear filters that encompasses many popular nonlinear filters like the well known Volterra filters, the Wiener nonlinear filters that derive from the truncation of the Wiener series [11], the Legendre nonlinear filters, the even mirror Fourier nonlinear filters, among many others [10].…”

Section: Introductionmentioning

confidence: 99%

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A Variable Step-Size for Sparse Nonlinear Adaptive Filters

Carini

Lima

Yazdanpanah

et al. 2021

2020 28th European Signal Processing Conference (EUSIPCO)

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The paper deals with the identification of nonlinear systems with adaptive filters. In particular, adaptive filters for functional link polynomial (FLiP) filters, a broad class of linearin-the-parameters (LIP) nonlinear filters, are considered. FLiP filters include many popular LIP filters, as the Volterra filters, the Wiener nonlinear filters, and many others. Given the large number of coefficients of these filters modeling real systems, especially for high orders, the solution is often very sparse. Thus, an adaptive filter exploiting sparsity is considered, the improved proportionate NLMS algorithm (IPNLMS), and an optimal stepsize is obtained for the filter. The optimal step-size alters the characteristics of the IPNLMS algorithm and provides a novel gradient descent adaptive filter. Simulation results involving the identification of a real nonlinear device illustrate the achievable performance in comparison with competing similar approaches.

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“…where f : R N 1 → R is a continuous function [9]. By definition, the order of a FLiP basis function is the sum of the orders of its factors g i [ξ].…”

Section: Flip Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Variable Step-Size for Sparse Nonlinear Adaptive Filters

Carini

Lima

Yazdanpanah

et al. 2021

2020 28th European Signal Processing Conference (EUSIPCO)

Self Cite

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“…Sequences are used in satellite telecommunication, cryptographic function design, wireless networks, signal processing, radar systems, and modern cell phones (see [7,14,20,26,33,37,39]). For instance, they are used in Code Division Multiple Access (CDMA), where the sequences with small correlation are important because a signal should not be affected by other signals in order to provide high-quality communication.…”

Section: Introductionmentioning

confidence: 99%

Partial direct product difference sets and almost quaternary sequences

Özden¹,

Yayla²

2023

AMC

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In this paper, we study the m-ary sequences with (non-consecutive) two zero-symbols and at most two distinct autocorrelation coefficients, which are known as almost m-ary nearly perfect sequences. We show that these sequences are equivalent to -partial direct product difference sets (PDPDS), then we extend known results on the sequences with two consecutive zerosymbols to non-consecutive case. Next, we study the notion of multipliers and orbit combination for -PDPDS. Finally, we present two construction methods for a family of almost quaternary sequences with at most two out-of-phase autocorrelation coefficients.

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“…T HE paper discusses a novel family of deterministic signals that can be used to identify any Functional Link Polynomial (FLiP) filter with the cross-correlation method. FLiP filters [1], [2] are a class of nonlinear filters that includes many of the most popular linear-in-the-parameters (LIP) nonlinear filters used in theory and practice [3]- [17]. The class of FLiP filters is very broad.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters

Carini

Orcioni

Terenzi

et al. 2020

IEEE Trans. Signal Process.

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The paper introduces a novel family of deterministic periodic signals, the orthogonal periodic sequences (OPSs), that allow the perfect identification on a finite period of any functional link polynomials (FLiP) filter with the cross-correlation method. The class of FLiP filters is very broad and includes many popular nonlinear filters, as the well-known Volterra and the Wiener nonlinear filters. The novel sequences share many properties of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of FLiP filters with the cross-correlation method. But, while PPSs exist only for orthogonal FLiP filters, the OPSs allow also the identification of non-orthogonal FLiP filters, as the Volterra filters. In OPSs, the modeled system input can be any persistently exciting sequence and can also be a quantized sequence. Moreover, OPSs can often identify FLiP filters with a sequence period and a computational complexity much smaller that PPSs. The provided experimental results, involving the identification of real devices and of a benchmark model, highlight the potentialities of the proposed OPSs in modeling unknown nonlinear systems.

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Nonlinear system identification using quasi-perfect periodic sequences

Cited by 11 publications

References 51 publications

A Variable Step-Size for Sparse Nonlinear Adaptive Filters

A Variable Step-Size for Sparse Nonlinear Adaptive Filters

Partial direct product difference sets and almost quaternary sequences

Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters

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