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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Печать: 1064-2315
ISSN Онлайн: 2163-9337

Выпуски:
Том 52, 2020 Том 51, 2019 Том 50, 2018 Том 49, 2017 Том 48, 2016 Том 47, 2015 Том 46, 2014 Том 45, 2013 Том 44, 2012 Том 43, 2011 Том 42, 2010 Том 41, 2009 Том 40, 2008 Том 39, 2007 Том 38, 2006 Том 37, 2005 Том 36, 2004 Том 35, 2003 Том 34, 2002 Том 33, 2001 Том 32, 2000 Том 31, 1999 Том 30, 1998 Том 29, 1997 Том 28, 1996

Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v46.i1.20
pages 9-19

Hybrid Algorithm for Identification of Linear by States Hammerstein Model

Fedor G. Garashchenko
Kyiv National Taras Shevchenko University, Kyiv, Ukraine
Olga G. Moroz
Kiev National Taras Shevchenko University

Краткое описание

The hybrid algorithm is proposed for identification of nonlinear dynamic Hammerstein model where static nonlinearity is modeled with radial basis functions neural network (RBFNN) and linear dynamic part − with a state-space model. The algorithm uses a particle swarm optimization (PSO) for RBFNN parameters estimating and subspace identification (SI) − for estimating the linear part parameters. Numerical example demonstrates the effectiveness of the PSO/SI algorithm proposed.


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