Parameter estimation using biologically inspired methods

Author

Lin, Weixing ; Rong Liu ; Liu, Rong ; Meng, Max Q -H

Author_Institution

Fac. of Inf. Sci. & Technol., Ningbo Univ., Ningbo

fYear

2007

fDate

15-18 Dec. 2007

Firstpage

1339

Lastpage

1343

Abstract

Identification of nonlinear systems has drawn much attention in recent years. This paper presents a new identification algorithm for Hammerstein models based on bacterial foraging. In specific, the biomimicry of the bacterial chemotaxis algorithm is used to identify model parameters. A flowchart of this identification algorithm is given. Simulation and Comparison studies show that the proposed bacterial foraging based approach outperforms the particle swarm optimization in terms of both convergence and precision.

Keywords

convergence; nonlinear systems; parameter estimation; particle swarm optimisation; Hammerstein models; bacterial chemotaxis algorithm; bacterial foraging; biologically inspired method; convergence; nonlinear systems identification; parameter estimation; particle swarm optimization; Ant colony optimization; Biomedical engineering; Biomimetics; Microorganisms; Nonlinear systems; Optimization methods; Parameter estimation; Particle swarm optimization; Robots; System identification; Bacterial foraging; Identification; Input nonlinear system; Particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on

Conference_Location

Sanya

Print_ISBN

978-1-4244-1761-2

Electronic_ISBN

978-1-4244-1758-2

Type

conf

DOI

10.1109/ROBIO.2007.4522358

Filename

4522358