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Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Imprimir: 1064-2315
ISSN En Línea: 2163-9337

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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

SINOPSIS

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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