Parameter estimation of fractional dynamical models arising from biological systems using an improved cuckoo search algorithm
WEI Jia-min
YU Yong-guang
ZHANG Shuo
Abstract：Recently,parameter estimation of nonlinear fractional-order systems has attracted great interest among many fields of science and engineering,especially computational biology.In this paper,we consider fractional dynamical models arising from biological systems,and parameter estimation of which is converted into a multi-dimensional optimization problem by treating both systematic parameters and fractional derivative orders as independent unknown parameters to be estimated.Moreover,an improved cuckoo search (ICS) algorithm is proposed as a novel technique to solve the problem of parameter estimation.In ICS,a simple adaptive parameter control mechanism is introduced,at the mean time,the opposition-based learning method is incorporated to the presented algorithm so that it can accelerate convergence speed and improve the accuracy of the estimated values.Numerical simulations are carried out on three typical fractional-order dynamical biological systems.We also investigate the condition with measurement error and noisy data.The simulation results demonstrate the effectiveness and efficiency of ICS,and show its significant superiority to the other methods.Thus,ICS may be deemed to be a promising tool for parameter estimation of nonlinear fractional-order systems.
Keywords：parameter estimationfractional dynamical modelscuckoo search algorithmadaptive parameter controlcompetence induction
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 1227-1238 )