Parameter Identification of Chaotic Dynamic Systems through an Improved Particle Swarm Optimization
This paper is concerned with the parameter identification problem for chaotic dynamic systems. An improved particle swarm optimization (IPSO), which is a novel evolutionary computation technique, is proposed to solve this problem. The feasibility of this approach is demonstrated through identifying the parameters of Lorenz chaotic system. The performance of the proposed IPSO is compared with the genetic algorithm (GA) and standard particle swarm optimization (SPSO) in terms of parameter accuracy and computational time. It is illustrated in simulations that the proposed IPSO is more successful than the SPSO and GA. IPSO is also improved to detect and determine the variation of parameters. In this case, a sentry particle is introduced to detect any changes in system parameters and if any change is detected, IPSO runs to find new optimal parameters. Hence, the proposed algorithm is a promising particle swarm optimization algorithm for system identification.
H. Modares et al., "Parameter Identification of Chaotic Dynamic Systems through an Improved Particle Swarm Optimization," Expert Systems with Applications, vol. 37, no. 5, pp. 3714-3720, Elsevier, May 2010.
The definitive version is available at https://doi.org/10.1016/j.eswa.2009.11.054
Electrical and Computer Engineering
Keywords and Phrases
Chaotic Dynamic Systems; Computational Time; Evolutionary Computation Techniques; Improved Particle Swarm Optimization; Lorenz Chaotic System; Optimal Parameter; Parameter Identification; Parameter Identification Problems; Particle Swarm Optimization Algorithm; Sentry Particles; System Identifications; Variation of Parameters; Chaotic Systems; Dynamic Programming; Genetic Algorithms; Parameter Estimation; Reactive Power; Particle Swarm Optimization (PSO); Particle Swarm Optimization
International Standard Serial Number (ISSN)
Article - Journal
© 2010 Elsevier, All rights reserved.
01 May 2010