Table of Contents Author Guidelines Submit a Manuscript
Discrete Dynamics in Nature and Society
Volume 2016, Article ID 3795961, 11 pages
http://dx.doi.org/10.1155/2016/3795961
Research Article

A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

1College of Information Science and Technology, Jinan University, Guangzhou 510632, China
2School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang 524088, China
3Department of Physics and Electronic Engineering, Guangxi Normal University for Nationalities, Chongzuo 532200, China
4School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
5Key Laboratory of Astronomical Optics & Technology, Nanjing Institute of Astronomical Optics & Technology, Chinese Academy of Sciences, Nanjing 210042, China

Received 11 August 2016; Revised 12 November 2016; Accepted 20 November 2016

Academic Editor: Stefan Balint

Copyright © 2016 Shaolong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.