文献类型：期刊文献

英文题名：General type-2 fuzzy multi-switching synchronization of fractional-order chaotic systems

作者：Sabzalian, Mohammad Hosein[1];Mohammadzadeh, Ardashir[2];Zhang, Weidong[1];Jermsittiparsert, Kittisak[3,4,5]

第一作者：Sabzalian, Mohammad Hosein

通讯作者：Zhang, WD[1];Mohammadzadeh, A[2];Jermsittiparsert, K[3];Jermsittiparsert, K[4];Jermsittiparsert, K[5]

机构：[1]Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China;[2]Univ Bonab, Dept Elect Engn, Bonab, Iran;[3]Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[4]Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam;[5]Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China

第一机构：Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China

通讯机构：[1]corresponding author), Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China;[2]corresponding author), Univ Bonab, Dept Elect Engn, Bonab, Iran;[3]corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[4]corresponding author), Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam;[5]corresponding author), Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China.|[1048419]河南财经政法大学MBA学院;[10484]河南财经政法大学;

年份：2021

卷号：100

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20210409812873);Scopus(收录号：2-s2.0-85099522862);WOS:【SCI-EXPANDED(收录号:WOS:000633002300013)】；

基金：This paper is partly supported by the National Science Foundation of China (61627810), National Key R&D Program of China (2017YFE0128500) and Key R&D Program of Guangdong (2020B1111010002).

语种：英文

外文关键词：Fractional order chaotic systems; Multi-switching; Robustness; Lyapunov theorem; Type-2 fuzzy neural network

摘要：In this study, the problem of multi-switching synchronization of the chaotic systems (CSs) with fractional-order dynamics is considered. Unlike to the most studies, the dynamics of slave chaotic systems are unknown and also the master systems are not fixed but are switched between several synchronization modes. A general type-2 (GT2) fuzzy neural network (FNN) is proposed to approximate the unknown nonlinearities. The stability and robustness is investigated by the Lyapunov theorem and the fractional-order adaptation laws are obtained to optimize the rules of GT2FNNs. The robustness of the proposed scheme against approximation errors and variation of synchronization modes is guaranteed by the proposed compensators. The good performance of schemed method is demonstrated by several simulations and comparison with recent presented control techniques.