Identification of continuous-time models for nonlinear dynamic systems from discrete data

DOI10.1080/00207721.2015.1069906zbMath1347.93263OpenAlexW2279708372MaRDI QIDQ2828760

Y. Z. Guo, Lingzhong Guo, Stephen A. Billings, Hua-Liang Wei

Publication date: 26 October 2016

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: http://eprints.whiterose.ac.uk/103208/1/Identification%20of%20Continuous%20Time%20Model%20for%20Nonlinear%20Dynamic%20Systems%20from%20Discrete%20Data%20-%20finalversion.pdf

zbMATH Keywords

nonlinear system identificationcontinuous-time modelorthogonal forward regressionIOFR algorithmmodulating function method

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Sampled-data control/observation systems (93C57) Identification in stochastic control theory (93E12)

Related Items (1)

A Galerkin discretisation-based identification for parameters in nonlinear mechanical systems

Cites Work

• Continuous-time approaches to system identification - a survey
• Parameter estimation for continuous-time models - a survey
• Modelling and identification of nonlinear deterministic systems in the delta-domain
• Nonlinear System Identification
• Continuous time non-linear system identification in the frequency domain
• Continuous-Time System Identification for Linear and Nonlinear Systems Using Wavelet Decompositions
• An iterative orthogonal forward regression algorithm
• Fitting ordinary differential equations to short time course data
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