Model Investigation of Nonlinear Dynamical Systems by Sparse Identification
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Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The sparse identification of nonlinear dynamics (SINDy), which is based on the sparse regression techniques to identify the nonlinear systems, is one of the recent data-driven model identification methods. The model equations of the system are extracted from the data. Although sufficient data is available from most of the engineering, healthcare, and economic sciences, there are few well-defined models to represent the system behaviour that can also be estimated from data-driven methods. With this motivation in mind, this study presents offline data-driven identification techniques to build the mathematical model of nonlinear systems. The data-based sparse identification of nonlinear systems is elaborated with a number of examples. The performance of the identification procedure is discussed in terms of quantitative metrics in the presence of noisy measurements.
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yazılım Mühendisliği, Fizik, Atomik ve Moleküler Kimya, Bilgisayar Bilimleri, Teori ve Metotlar, İstatistik ve Olasılık
Kaynak
Avrupa Bilim ve Teknoloji Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
0
Sayı
Ejosat Özel Sayı 2020 (ISMSIT)
