Parameter estimation of nonlinear chaotic system by improved TLBO strategy
文献类型：期刊论文
| 作者 | Zhang, Hongjun2; Li, Baozhu2; Zhang, Jun2; Qin, Yuanhui2; Feng, Xiaoyi1; Liu, Bo1
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| 刊名 | SOFT COMPUTING
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000386611200025 |
| 出版者 | SPRINGER |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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