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

Content Provider	Springer Nature Link
Author	Ko, Chia Nan Fu, Yu Yi Lee, Cheng Ming Wu, Chia Ju
Copyright Year	2010
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.
Starting Page	33
Ending Page	36
Page Count	4
File Format	PDF
ISSN	14335298
Journal	Artificial Life and Robotics
Volume Number	15
Issue Number	1
e-ISSN	16147456
Language	English
Publisher	Springer Japan
Publisher Date	2010-08-27
Publisher Place	Japan
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Particle swarm optimization Nonlinear timevarying evolution Orthogonal array Lorenz chaotic system Control , Robotics, Mechatronics Computation by Abstract Devices Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Biochemistry, Genetics and Molecular Biology

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