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A review on computational intelligence for identification of nonlinear dynamical systems

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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.

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Acknowledgements

GQ acknowledges the support from Sapienza University of Rome through the project n. RM116154C95F7DD0 titled “Smart solutions for the assessment of structures in seismic areas.” WL acknowledges the partial financial support from the PRIN Grant n. 2017L7X3CS titled “3D Printing: A bridge to the future. Computational methods, innovative applications, experimental validations of new materials and technologies.”

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  1. Department of Structural and Geotechnical Engineering, Sapienza University of Rome, Via Eudossiana 18, 00184, Rome, Italy

    Giuseppe Quaranta & Walter Lacarbonara

  2. Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, 3620 South Vermont Avenue, Los Angeles, CA, 90089, USA

    Sami F. Masri

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Quaranta, G., Lacarbonara, W. & Masri, S.F. A review on computational intelligence for identification of nonlinear dynamical systems. Nonlinear Dyn 99, 1709–1761 (2020). https://doi.org/10.1007/s11071-019-05430-7

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