Parameter estimation of nonlinear chaotic system by improved TLBO strategy

Estimation of parameters of chaotic systems is a subject of substantial and well-developed research issue in nonlinear science. From the viewpoint of optimization, parameter estimation can be formulated as a multi-modal constrained optimization problem with multiple decision variables. This investig...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 20; H. 12; S. 4965 - 4980
Hauptverfasser: Zhang, Hongjun, Li, Baozhu, Zhang, Jun, Qin, Yuanhui, Feng, Xiaoyi, Liu, Bo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2016
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Biogeography
Chaos theory
Computational Intelligence
Control
Engineering
Evolutionary algorithms
Genetic algorithms
Heuristic methods
Learning
Lorenz system
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Methods
Optimization
Parameter estimation
Parameter identification
Particle swarm optimization
Robotics
Students
Teaching
Time series
Memetic algorithm
Parameter estimation
System identification
Chaotic system
Nelder–Mead simplex algorithm
Teaching–learning-based optimization
ISSN:1432-7643, 1433-7479
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Zusammenfassung:Estimation of parameters of chaotic systems is a subject of substantial and well-developed research issue in nonlinear science. From the viewpoint of optimization, parameter estimation can be formulated as a multi-modal constrained optimization problem with multiple decision variables. This investigation makes a systematic examination of the feasibility of applying a newly proposed population-based optimization method labeled here as teaching–learning-based optimization (TLBO) to identify the unknown parameters for a class of chaotic system. The preliminary test demonstrates that despite its global fast coarse search capability, teaching–learning-based optimization often risks getting prematurely stuck in local optima. To enhance its fine (local) searching performance of TLBO, Nelder–Mead simplex algorithm-based local improvement is incorporated into TLBO so as to continually search for the global optima through the reflection, expansion, contraction, and shrink operators. Working with the well-established Lorenz system, we assess the effectiveness and efficiency of the proposed improved TLBO strategy. The empirical results indicate the success of the proposed hybrid approach in which the global exploration and the local exploitation are well balanced, providing the best solutions for all instances used over other state-of-the-art metaheuristics for chaotic identification in literature, including particle swarm optimization, genetic algorithm, and quantum-inspired evolutionary algorithm.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1786-2