Initial Estimation of Wiener-Hammerstein System with Random Forest

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Abstract

The most popular class of Volterra nonlinear dynamical system is Wiener-Hammerstein system. A static nonlinearity, positioned between two dynamical sub-system constructs Wiener-Hammerstein (W-H) system. The best linear approximation (BLA) technique assembles two linear filters and the nonlinearity into a single filter for input and noisy output. The identification challenge resides in separating two filters. This work proposes Random Forest (RF) as a first alternative to do this. It is like the selection of holiday destinations based on the recommendation of random traveller. The proposed technique supports reasonably high noise level and does not require to optimize all models. Thus, a speedup in processing time is achieved without any prior knowledge about the model configuration.

Original languageEnglish
Title of host publication2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
Pages1842-1847
Number of pages6
ISBN (Electronic)9781538634608
DOIs
Publication statusPublished - May 2019
Event2019 IEEE International Instrumentation and Measurement Technology Conference - Auckland, New Zealand
Duration: 20 May 201923 May 2019

Publication series

NameI2MTC 2019 - 2019 IEEE International Instrumentation and Measurement Technology Conference, Proceedings
Volume2019-May

Conference

Conference2019 IEEE International Instrumentation and Measurement Technology Conference
CountryNew Zealand
CityAuckland
Period20/05/1923/05/19

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