Identification of Hammerstein Nonlinear Stochastic Systems

Authors:
G. R. Bolkvadze
Affiliations:
Institute of Cybernetics, Georgian Academy of Sciences, Tbilisi, Georgia
Venue:
Automation and Remote Control
Year:
2002

Citing 0
Cited 3

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The Hammerstein–Wiener Model for Identification of Stochastic Systems

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A Method of Dispersion Statistical Linearization of Nonlinear Stochastic Systems of the Hammerstein Class

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Abstract

Identification of Hammerstein stochastic dynamic systems is investigated. For this problem, the nonlinearities of the system must be taken into account. The solution is derived by recurrent gradient identification algorithms based on the Newton–Raphson and least-squares methods. Convergence is demonstrated, convergence rate is estimated, and parameter estimation accuracy is derived. The procedure is shown to be effective in practice.