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- W3102913089 endingPage "20180534" @default.
- W3102913089 startingPage "20180534" @default.
- W3102913089 abstract "Hybrid systems are traditionally difficult to identify and analyse using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely focus on identifying a single set of governing equations solely from measurement data. In this article, we develop a new methodology, Hybrid-Sparse Identification of Nonlinear Dynamics, which identifies separate nonlinear dynamical regimes, employs information theory to manage uncertainty and characterizes switching behaviour. Specifically, we use the nonlinear geometry of data collected from a complex system to construct a set of coordinates based on measurement data and augmented variables. Clustering the data in these measurement-based coordinates enables the identification of nonlinear hybrid systems. This methodology broadly empowers nonlinear system identification without constraining the data locally in time and has direct connections to hybrid systems theory. We demonstrate the success of this method on numerical examples including a mass–spring hopping model and an infectious disease model. Characterizing complex systems that switch between dynamic behaviours is integral to overcoming modern challenges such as eradication of infectious diseases, the design of efficient legged robots and the protection of cyber infrastructures." @default.
- W3102913089 created "2020-11-23" @default.
- W3102913089 creator A5009969698 @default.
- W3102913089 creator A5029587656 @default.
- W3102913089 creator A5066978688 @default.
- W3102913089 creator A5075972178 @default.
- W3102913089 creator A5083450863 @default.
- W3102913089 date "2019-03-01" @default.
- W3102913089 modified "2023-10-18" @default.
- W3102913089 title "Model selection for hybrid dynamical systems via sparse regression" @default.
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- W3102913089 doi "https://doi.org/10.1098/rspa.2018.0534" @default.
- W3102913089 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6451978" @default.
- W3102913089 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31007544" @default.
- W3102913089 hasPublicationYear "2019" @default.