Fixed-time synchronization of fractional-order complex-valued neural networks with time-varying delay via sliding mode control

被引:20
|
作者
Cheng, Yali [1 ,2 ]
Hu, Taotao [3 ]
Xu, Wenbo [1 ]
Zhang, Xiaojun [4 ]
Zhong, Shouming [4 ]
机构
[1] Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China
[2] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst, Huzhou 313001, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 611731, Peoples R China
[4] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China
关键词
Fractional-order; Complex-valued neural networks; Fixed-time synchronization; Sliding mode control; Time-varying delay; STABILITY ANALYSIS; FINITE-TIME; SYSTEMS; STABILIZATION; DESIGN;
D O I
10.1016/j.neucom.2022.07.015
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Taking into account fractional-order complex-valued neural networks with time-varying delay, the issue of achieving fixed-time synchronization is discussed in this paper. By utilizing the properties of fractional calculus and fractional-order comparison principle, an improved lemma is proposed to derive the fixed-time synchronization conditions. On the basis of sliding model control and Lyapunov stability theorem, an effective sliding mode surface is constructed, which only uses the synchronization error information of FOCVNNs and is composed of fractional and integer integral terms. Further, a suitable sliding model con-trol is constructed, which makes synchronization error converges to zero in a fixed-time. Beyond that, several sufficient conditions are posed to guarantee fixed-time synchronization of the fractional-order complex-valued neural networks and the upper bound of synchronization settling time is estimated. Finally, two numerical simulations are given to demonstrate the effectiveness of the presented theoret-ical results.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页码:339 / 352
页数:14
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