Journal article

Parameter estimation of delay differential equations: An integration-free LS-SVM approach

Siamak Mehrkanoon, Saeid Mehrkanoon, Johan AK Suykens

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | ELSEVIER | Published : 2014

Abstract

This paper introduces an estimation method based on Least Squares Support Vector Machines (LS-SVMs) for approximating time-varying as well as constant parameters in deterministic parameter-affine delay differential equations (DDEs). The proposed method reduces the parameter estimation problem to an algebraic optimization problem. Thus, as opposed to conventional approaches, it avoids iterative simulation of the given dynamical system and therefore a significant speedup can be achieved in the parameter estimation procedure. The solution obtained by the proposed approach can be further utilized for initialization of the conventional nonconvex optimization methods for parameter estimation of DD..

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Grants

Awarded by Research Council KUL


Funding Acknowledgements

This work was supported by Research Council KUL: GOA/11/05 Ambiorics, GOA/10/09 MaNet, CoE EF/05/006 Optimization in Engineering (OPTEC), IOF-SCORES4CHEM, several PhD/postdoc & fellow grants; Flemish Government: FWO: PhD/postdoc Grants, projects: G0226.06 (cooperative systems and optimization), G0321.06 (Tensors), G. 0302.07 (SVM/Kernel), G.0320.08 (convex MPC), G.0558.08 (Robust MHE), G. 0557.08 (Glycemia2), G.0588.09 (Brain-machine), G.0377.12 (structured models) research communities (WOG: ICCoS, ANMMM, MLDM); G.0377.09 (Mechatronics MPC) IWT: PhD Grants, Eureka- Flite+, SBO LeCoPro, SBO Climaqs, SBO POM, O & O-Dsquare; Belgian Federal Science Policy Office: IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007-2011); EU: ERNSI; FP7-HD-MPC (INFSO-ICT-223854), COST intelliCIS, FP7-EMBOCON (ICT-248940); Contract Research: AMINAL; Other: Helmholtz: viCERP, ACCM, Bauknecht, Hoerbiger, ERC AdG A-DATADRIVE-B. Johan Suykens is a professor at the KU Leuven, Belgium.