期刊
PHYSICS LETTERS A
卷 380, 期 11-12, 页码 1164-1171出版社
ELSEVIER
DOI: 10.1016/j.physleta.2016.01.040
关键词
Chaotic systems; Parameter estimation; Particle swarm optimization; Ant colony optimization
资金
- University of La Serena [PI13141]
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO-ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO-ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO-ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO-ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. (C) 2016 Elsevier B.V. All rights reserved.
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