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Bibliographic Details
Main Author: Wilson, Dan
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2405.03668
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author Wilson, Dan
author_facet Wilson, Dan
contents A data-driven model identification strategy is developed for dynamical systems near a supercritical Hopf bifurcation with nonautonomous inputs. This strategy draws on phase-amplitude reduction techniques, leveraging an analytical representation for the phase and amplitude response curves of the Hopf normal form to infer system parameters. Fitting can be performed by recording the system output during the relaxation to the stable limit cycle after applying as few as two carefully timed pulse inputs. This strategy is illustrated in two examples with relevance to circadian oscillations. In each example, the proposed model identification strategy allows for the formulation, solution, and implementation of a closed loop nonlinear optimal control problem.
format Preprint
id arxiv_https___arxiv_org_abs_2405_03668
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Data-Driven Model Identification Near a Supercritical Hopf Bifurcation Using Phase-Based Approaches
Wilson, Dan
Dynamical Systems
A data-driven model identification strategy is developed for dynamical systems near a supercritical Hopf bifurcation with nonautonomous inputs. This strategy draws on phase-amplitude reduction techniques, leveraging an analytical representation for the phase and amplitude response curves of the Hopf normal form to infer system parameters. Fitting can be performed by recording the system output during the relaxation to the stable limit cycle after applying as few as two carefully timed pulse inputs. This strategy is illustrated in two examples with relevance to circadian oscillations. In each example, the proposed model identification strategy allows for the formulation, solution, and implementation of a closed loop nonlinear optimal control problem.
title Data-Driven Model Identification Near a Supercritical Hopf Bifurcation Using Phase-Based Approaches
topic Dynamical Systems
url https://arxiv.org/abs/2405.03668