Parameter estimation of chaotic systems by a nonlinear time-varying evolution PSO method

Authors:
Chia-Nan Ko;Yu-Yi Fu;Cheng-Ming Lee;Chia-Ju Wu
Affiliations:
Department of Automation Engineering, Nan-Kai University of Technology, Tasotun, Nantou, Taiwan 542;Department of Automation Engineering, Nan-Kai University of Technology, Tasotun, Nantou, Taiwan 542;Department of Computer and Communication Engineering, Nan-Kai University of Technology, Tasotun, Nantou, Taiwan;Department of Electrical Engineering, National Yunlin University of Science and Technology, Douliou, Yunlin, Taiwan
Venue:
Artificial Life and Robotics
Year:
2010

Citing 4
Cited 0

Obtaining initial parameter estimates for chaotic dynamical systems using linear associative memories

Applied Mathematics and Computation
Comparison among five evolutionary-based optimization algorithms

Advanced Engineering Informatics
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization

Computers and Operations Research
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

An important issue in nonlinear science is parameter estimation for Lorenz chaotic systems. There has been increasing interest in this issue in various research fields, and it could essentially be formulated as a multidimensional optimization problem. A novel evolutionary computation algorithm, nonlinear time-varying evolution particle swarm optimization (NTVEPSO), is employed to estimate these parameters. In the NTVEPSO method, the nonlinear time-varying evolution functions are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates tothe full factorial experiments. The NTVEPSO method and other PSO methods are then applied to identify the Lorenz chaotic system. Simulation results demonstrate the feasibility and superiority of the proposed NTVEPSO method.