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DOI 10.34229/KCA2522-9664.26.5.13
UDC 517.9:004.852

A.A. Rutkas
Kharkiv National University of Radio Electronics, Kharkiv, Ukraine,
andrew@rutrus.com


NEURAL NETWORK-BASED STATE MODELING OF NONLINEAR
DISCRETE DESCRIPTOR SYSTEMS

Abstract. A method for state modelling of nonlinear discrete-time descriptor dynamic systems based on artificial neural networks is proposed. The system dynamics is described by a nonlinear vector difference equation not solved for the state at the highest step. Trial states depending on adjustable parameters (weight matrices and bias vectors) are generated using a recurrent neural network. The results are illustrated by model examples, specifically, by a Leontief-type input-output production model.

Keywords: nonlinear discrete descriptor dynamic system, vector difference equation, regular matrix pencil, recurrent neural network, trial state.


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