Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems |
| |
Authors: | Maria Rodriguez-Fernandez Jose A Egea and Julio R Banga |
| |
Institution: | (1) Process Engineering Group, Instituto de Investigaciones Marinas (C.S.I.C.), Spanish Council for Scientific Research, C/Eduardo Cabello, 6, 36208 Vigo, Spain |
| |
Abstract: | Background We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due
to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless
initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization
(GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic
size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising
results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic
GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was
to further reduce the computational effort without loosing robustness. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|