中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 重庆绿色智能技术研究院
  3. 中国科学院重庆绿色智能技术研究院
Convergence and robustness of bounded recurrent neural networks for solving dynamic Lyapunov equations

文献类型：期刊论文

作者Wang, Guancheng2,3; Hao, Zhihao2; Zhang, Bob2; Jin, Long1
刊名INFORMATION SCIENCES
出版日期2022-04-01
卷号588页码:106-123
关键词Recurrent neural network dynamic Lyapunov equations Bounded activation functions Finite-time convergence Robustness
ISSN号0020-0255
DOI10.1016/j.ins.2021.12.039
通讯作者Zhang, Bob(bobzhang@um.edu.mo) ; Jin, Long(jinlongsysu@foxmail.com)
英文摘要Recurrent neural networks have been reported as an effective approach to solve dynamic Lyapunov equations, which widely exist in various application fields. Considering that a bounded activation function should be imposed on recurrent neural networks to solve the dynamic Lyapunov equation in certain situations, a novel bounded recurrent neural network is defined in this paper. Following the definition, several bounded activation func-tions are proposed, and two of them are used to construct the bounded recurrent neural network for demonstration, where one activation function has a finite-time convergence property and the other achieves robustness against noise. Moreover, theoretical analyses provide rigorous and detailed proof of these superior properties. Finally, extensive simula-tion results, including comparative numerical simulations and two application examples, are demonstrated to verify the effectiveness and feasibility of the proposed bounded recur-rent neural network.(c) 2021 Elsevier Inc. All rights reserved.
资助项目University of Macau[MYRG2018-00053-FST] ; CAS Light of West ChinaProgram ; Natural Science Foundation of Chongqing (China)[Cstc2020jcyj-zdxmX0028] ; National Natural Science Foundation of China[62072121] ; first class discipline construction platform project in 2019 of Guangdong Ocean University[231419026] ; Youth Innovation Project of the Department of Education of Guangdong Province[2020KQNCX026] ; Open Research Fund of the Beijing Key Laboratory of Big Data Technology for Food Safety[BTBD-2021KF05] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515011847] ; Special Project in Key Fields of Universities in Department of Education of Guangdong Province[2019KZDZX1036] ; Guangdong Graduate Education Innovation Project, Graduate Summer School[2020SQXX19] ; Guangdong Graduate Education Innovation Project, Graduate Academic Forum[202160] ; Key Lab of Digital Signal and Image Processing of Guangdong Province[2019GDDSIPL-01]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000768300300006
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.138/handle/2HOD01W0/15469]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Zhang, Bob; Jin, Long
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
2.Univ Macau, Dept Comp & Informat Sci, Taipa 999078, Macau, Peoples R China
3.Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Guancheng,Hao, Zhihao,Zhang, Bob,et al. Convergence and robustness of bounded recurrent neural networks for solving dynamic Lyapunov equations[J]. INFORMATION SCIENCES,2022,588:106-123.
APA Wang, Guancheng,Hao, Zhihao,Zhang, Bob,&Jin, Long.(2022).Convergence and robustness of bounded recurrent neural networks for solving dynamic Lyapunov equations.INFORMATION SCIENCES,588,106-123.
MLA Wang, Guancheng,et al."Convergence and robustness of bounded recurrent neural networks for solving dynamic Lyapunov equations".INFORMATION SCIENCES 588(2022):106-123.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。