Parameter identification of chaotic dynamic systems through an improved particle swarm optimization

Authors:
Hamidreza Modares;Alireza Alfi;Mohammad-Mehdi Fateh
Affiliations:
Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood 36199-95161, Iran;Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood 36199-95161, Iran;Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood 36199-95161, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 6
Cited 6

Parameter Selection in Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A PSO method with nonlinear time-varying evolution based on neural network for design of optimal harmonic filters

Expert Systems with Applications: An International Journal
Synchronization of optical chaos in vertical-cavity surface-emitting lasers via optimal PI controller

Expert Systems with Applications: An International Journal
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization

Computers and Operations Research
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

IEEE Transactions on Evolutionary Computation

Solving nonlinear optimal control problems using a hybrid IPSO-SQP algorithm

Engineering Applications of Artificial Intelligence
Intelligent identification and control using improved fuzzy particle swarm optimization

Expert Systems with Applications: An International Journal
Parameters identification of nonlinear state space model of synchronous generator

Engineering Applications of Artificial Intelligence
Global optimization of an optical chaotic system by Chaotic Multi Swarm Particle Swarm Optimization

Expert Systems with Applications: An International Journal
Development and investigation of efficient artificial bee colony algorithm for numerical function optimization

Applied Soft Computing
Piecewise function based gravitational search algorithm and its application on parameter identification of AVR system

Neurocomputing

Quantified Score

Hi-index	12.05

Visualization

Abstract

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.