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Parameter estimation of nonlinear chaotic system by improved TLBO strategy
Zhang, Hongjun2; Li, Baozhu2; Zhang, Jun2; Qin, Yuanhui2; Feng, Xiaoyi1; Liu, Bo1
Source PublicationSOFT COMPUTING
AbstractEstimation 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.
KeywordParameter estimation System identification Chaotic system Teaching-learning-based optimization Nelder-Mead simplex algorithm Memetic algorithm
Funding ProjectNational Natural Science Foundation of China[71101139] ; National Natural Science Foundation of China[71103013] ; National Natural Science Foundation of China[71390330] ; State Key Laboratory of Intelligent Control and Decision of Complex Systems of Beijing Institute ofTechnology ; Defense Industrial Technology Development Program
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000386611200025
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Document Type期刊论文
Corresponding AuthorLiu, Bo
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Syst Engn Res Inst, Beijing 100094, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Hongjun,Li, Baozhu,Zhang, Jun,et al. Parameter estimation of nonlinear chaotic system by improved TLBO strategy[J]. SOFT COMPUTING,2016,20(12):4965-4980.
APA Zhang, Hongjun,Li, Baozhu,Zhang, Jun,Qin, Yuanhui,Feng, Xiaoyi,&Liu, Bo.(2016).Parameter estimation of nonlinear chaotic system by improved TLBO strategy.SOFT COMPUTING,20(12),4965-4980.
MLA Zhang, Hongjun,et al."Parameter estimation of nonlinear chaotic system by improved TLBO strategy".SOFT COMPUTING 20.12(2016):4965-4980.
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