Parameter estimation of nonlinear chaotic system by improved TLBO strategy

doi:10.1007/s00500-015-1786-2

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 系统科学研究所

	Parameter estimation of nonlinear chaotic system by improved TLBO strategy
	Zhang, Hongjun 2; Li, Baozhu 2; Zhang, Jun 2; Qin, Yuanhui 2; Feng, Xiaoyi 1; Liu, Bo1
刊名	SOFT COMPUTING
	2016-12-01
卷号	20 期号:12 页码:4965-4980
关键词	Parameter estimation System identification Chaotic system Teaching-learning-based optimization Nelder-Mead simplex algorithm Memetic algorithm
ISSN号	1432-7643
DOI	10.1007/s00500-015-1786-2
英文摘要	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.
资助项目	National 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研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000386611200025
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/23902]
专题	系统科学研究所
通讯作者	Liu, Bo
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Syst Engn Res Inst, Beijing 100094, Peoples R China
推荐引用方式 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.