Affiliation:
1. TICAM, The University of Texas, Austin, TX ACES 6430, USA
Abstract
We consider the problem of frequency domain kernel estimation using random multi-tone (harmonic) excitation for 2nd-order Volterra models. The basic approach is based on least squares minimization of model output error, and results for the Volterra kernel estimations with random multi-tone inputs and random Gaussian input are compared. We show that kernel estimation with multi-tones are very accurate and efficient compared to the latter. As an illustration, the proposed method is applied to a discrete input–output system obtained from the numerical simulation of a representative hydrodynamic system for modeling semiconductor device transport. We also consider the effect of noise in the kernel estimation.
Funder
National Science Foundation
Subject
Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Hardware and Architecture
Cited by
5 articles.
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