@article {Cerini:2023:0736-2935:61,
title = "Structural system modeling from base excitation measurements using Swarm Intelligence",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2023",
volume = "267",
number = "1",
publication date ="2023-11-05T00:00:00",
pages = "61-64",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2023/00000267/00000001/art00008",
doi = "doi:10.3397/NO_2023_0017",
author = "Cerini, Corinna",
abstract = "The problem of system identification is classified as an inverse problem, and it concerns the derivation of mathematical models from experimental data. Problems of this kind can be addressed using optimization techniques. This paper utilizes a hybrid form of the well-known Particle
Swarm Algorithm (hPSO) to produce a mathematical model representative of the dynamic behaviour of a generic structure subjected to a base excitation, directly from acceleration measurements. The proposed algorithm is specifically tailored for this application; physical properties of the matrices
are assured introducing a 'healing' process. Problem-specific operators from Evolutionary Strategies are used to improve the solution and avoid a local minimum. The final adjustment of the solution is delegated to a local optimization algorithm.",
}