文献类型：期刊文献

英文题名：Identification of fractional-order systems with unknown initial values and structure

作者：Du, Wei[1];Miao, Qingying[2];Tong, Le[3];Tang, Yang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Continuing Educ, Shanghai 200030, Peoples R China;[3]Hong Kong Polytech Univ, Fac Appl Sci & Text, Hong Kong, Hong Kong, Peoples R China

年份：2017

卷号：381

期号：23

起止页码：1943

外文期刊名：PHYSICS LETTERS A

收录：;EI(收录号：20202208736791);WOS:【SCI-EXPANDED(收录号:WOS:000401379600004)】；

基金：This research was supported in part by the National Natural Science Foundation of China under Grant No. 61473189, 61590923, 61673176, in part by the China Postdoctoral Science Foundation under Grant No. 2016M601525, in part by the Fundamental Research Funds for the Central Universities under Grant No. 222201714028, and in part by Shanghai Sailing Program under Grant No. 17YF1427700.

语种：英文

外文关键词：Fractional-order chaotic systems; Differential evolution; Nonlinear optimization; System identification; Synchronization

摘要：In this paper, the identification problem of fractional-order chaotic systems is proposed and investigated via an evolutionary optimization approach. Different with other studies to date, this research focuses on the identification of fractional-order chaotic systems with not only unknown orders and parameters, but also unknown initial values and structure. A group of fractional-order chaotic systems, i.e., Lorenz,Lu, Chen, Rossler, Arneodo and Volta chaotic systems, are set as the system candidate pool. The identification problem of fractional-order chaotic systems in this research belongs to mixed integer nonlinear optimization in essence. A powerful evolutionary algorithm called composite differential evolution (CoDE) is introduced for the identification problem presented in this paper. Extensive experiments are carried out to show that the fractional-order chaotic systems with unknown initial values and structure can be successfully identified by means of CoDE. (C) 2017 Elsevier B.V. All rights reserved.