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Identification of Linear Systems with Nonlinear Distortions

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TLDR
A theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'.
Abstract
This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.

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Citations
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Extracting a Non-parametric Instantaneous FRF of a Linear, Slowly Time-Varying system using a Multisine Excitation

TL;DR: In this article, a non-parametric method for extracting information about the instantaneous dynamics of a slowly time-varying system is proposed. But the method assumes that the system is described by a linear ordinary differential equation whose coefficients are varying piecewise linearly with time, and these variations are slow w.r.t.
Journal ArticleDOI

Analysis of the nonlinear induced variance in linear system identification

TL;DR: In this article, the variance of the nonparametric frequency response function (FRF) can still be obtained from the experimental data using the classical variance formulas, however, for parametric estimates Ĝ BLA ( q, θ), an increased variance is observed.
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New connections between frequency response functions for a class of nonlinear systems

TL;DR: In this paper, a relation between different frequency response functions has been established and sufficient conditions for this relation to exist and results on uniqueness and equivalence of the HOSIDF and GFRF are provided.
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Improved crest factor minimization of multisine excitation signals using nonlinear optimization

TL;DR: In this paper , a two-step method is proposed for achieving a lower Crest Factor (CF) compared to state-of-the-art methods, starting with Guillaume's method and solving a slack reformulation of the ∞-norm minimization problem for additional improvement.
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Nonlinear noise spectrum measurement using a probability-maintained noise power ratio method

TL;DR: In this article , a probability-maintained (PM) NPR method is proposed to accurately measure the spectrum of nonlinear noise via a spectrum analyzer in non-specific systems, including systems with non-Gaussian stimuli.
References
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Lennart Ljung
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Jorma Rissanen
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TL;DR: The number of digits it takes to write down an observed sequence x1,...,xN of a time series depends on the model with its parameters that one assumes to have generated the observed data.
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Engineering Applications of Correlation and Spectral Analysis

TL;DR: This chapter discusses single-Input/Single-Output Relationships, nonstationary data analysis techniques, and procedures to Solve Multiple- Input/Multiple-Output Problems.
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Nonlinear black-box modeling in system identification: a unified overview

TL;DR: What are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques are described, from a user's perspective.
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