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Journal ArticleDOI

Orthogonalised frequency domain Volterra model for non-Gaussian inputs

S.B. Kim, +1 more
- Vol. 140, Iss: 6, pp 402-409
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TLDR
An orthogonalised Volterra system model, valid for both non-Gaussian and Gaussian inputs, is presented and used to model the linear and quadratic responses of a tension leg platform subject to random seas, given experimental input-output time series data.
Abstract
An orthogonalised Volterra system model, valid for both non-Gaussian and Gaussian inputs, is presented. The approach is based on ordered sets of conditioned orthogonal higherorder input vectors in the frequency domain, and utilises co-ordinate transformation to relate the orthogonal and nonorthogonal system models. The orthogonal model exhibits no interference effects, thus facilitating physical interpretation of the nonlinear system model. The importance of non-Gaussian excitation in the nonlinear system identification procedure is discussed. The performance of the orthogonalised Volterra model is measured in terms of a generalised nonlinear system coherence function, and compared with the results of the Wiener (for Gaussian input) and Volterra models. The advantages of the orthogonalised Volterra model are illustrated by using it to model the linear and quadratic responses of a tension leg platform subject to random seas, given experimental input-output time series data.

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Citations
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Journal ArticleDOI

A bibliography on nonlinear system identification

TL;DR: The present bibliography represents a comprehensive list of references on nonlinear system identification and its applications in signal processing, communications, and biomedical engineering.
Journal ArticleDOI

Estimation of co-channel nonlinear distortion and SNDR in wireless systems

TL;DR: In this paper, the effective signal-to-noise and distortion ratio (SNDR) at the output of a nonlinear amplifier is defined through the decomposition of the nonlinear output into correlated output and uncorrelated distortion.
Journal ArticleDOI

Analysis of the SAR imaging process of the ocean surface using Volterra models

TL;DR: In this article, a decomposition based on a Volterra model is proposed to decompose the nonlinear distortion mechanisms of the SAR spectrum over different spectra of polynomial interactions.
Journal ArticleDOI

Experimental study on the nonlinear pressure acting on a high-speed vessel in regular waves

TL;DR: In this paper, an approximate third-order Volterra model is applied to handle the statistics of some nonlinear seakeeping problems, such as motions and vertical hull girder loads.
Journal ArticleDOI

Adaptive weighted least squares algorithm for Volterra signal modeling

TL;DR: In this paper, an adaptive weighted least squares (AWLS) algorithm was proposed for the estimation of both stationary and non-stationary signals which arise from Volterra models.
References
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Book

Nonlinear Problems in Random Theory

TL;DR: A series of lectures on the role of nonlinear processes in physics, mathematics, electrical engineering, physiology, and communication theory was given in this article, where the last few of these were devoted to the application of my ideas to problems in the statistical mechanics of gases.
Journal ArticleDOI

Digital Bispectral Analysis and Its Applications to Nonlinear Wave Interactions

TL;DR: The bispectrum, which is an ensemble average of a product of three spectral components, is shown to be a very useful diagnostic tool in experimental studies of nonlinear wave interactions in random media.
Book

Theory of Functions

Konrad Knopp
Journal ArticleDOI

A test for linearity of stationary time series

TL;DR: In this paper, the authors describe statistical tests for testing the assumption that the series conforms to a linear model, based on the bispectral density function, and demonstrate these tests with two real time series and four simulated time series.
Journal ArticleDOI

A digital method of modeling quadratically nonlinear systems with a general random input

TL;DR: Without assuming particular statistics of the input, a practical digital method of estimating linear and quadratic transfer functions of a nonlinear time-invariant system that can be described by Volterra series of up to second order is presented.
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