This work aims to provide a broad overview of computational techniques belonging to the area of artificial intelligence tailored for identification of nonlinear dynamical systems. Both parametric and nonparametric identification problems are considered. The examined computational intelligence techniques for parametric identification deal with genetic algorithm, particle swarm optimization, and differential evolution. Special attention is paid to the parameters estimation for a rich class of nonlinear dynamical models, including the Bouc–Wen model, chaotic systems, the Jiles–Atherton model, the LuGre model, the Prandtl–Ishlinskii model, the Preisach model, and the Wiener–Hammerstein model. On the other hand, genetic programming and artificial neural networks are discussed for nonparametric identification applications. Once the identification problem is formulated, a detailed illustration of the considered computational intelligence techniques is provided, together with a comprehensive examination of relevant applications in the fields of structural mechanics and engineering. Possible directions for future research are also addressed.

A review on computational intelligence for identification of nonlinear dynamical systems / Quaranta, G.; Lacarbonara, W.; Masri Sami, F.. - In: NONLINEAR DYNAMICS. - ISSN 0924-090X. - 99:2(2020), pp. 1709-1761. [10.1007/s11071-019-05430-7]

A review on computational intelligence for identification of nonlinear dynamical systems

Quaranta G.;Lacarbonara W.;
2020

Abstract

This work aims to provide a broad overview of computational techniques belonging to the area of artificial intelligence tailored for identification of nonlinear dynamical systems. Both parametric and nonparametric identification problems are considered. The examined computational intelligence techniques for parametric identification deal with genetic algorithm, particle swarm optimization, and differential evolution. Special attention is paid to the parameters estimation for a rich class of nonlinear dynamical models, including the Bouc–Wen model, chaotic systems, the Jiles–Atherton model, the LuGre model, the Prandtl–Ishlinskii model, the Preisach model, and the Wiener–Hammerstein model. On the other hand, genetic programming and artificial neural networks are discussed for nonparametric identification applications. Once the identification problem is formulated, a detailed illustration of the considered computational intelligence techniques is provided, together with a comprehensive examination of relevant applications in the fields of structural mechanics and engineering. Possible directions for future research are also addressed.
2020
Artificial neural network · Computational intelligence · Differential evolution · Genetic algorithm · Genetic programming · Nonlinear system identification · Particle swarm optimization
01 Pubblicazione su rivista::01a Articolo in rivista
A review on computational intelligence for identification of nonlinear dynamical systems / Quaranta, G.; Lacarbonara, W.; Masri Sami, F.. - In: NONLINEAR DYNAMICS. - ISSN 0924-090X. - 99:2(2020), pp. 1709-1761. [10.1007/s11071-019-05430-7]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1350989
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