Observability of Automata Networks: Fixed and Switching Cases

doi:10.1109/TNNLS.2017.2651053

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	Observability of Automata Networks: Fixed and Switching Cases
	Li, Rui 1; Hong, Yiguang2 ; Wang, Xingyuan 3
	2018-04-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	29 期号:4 页码:1388-1394
摘要	Automata networks are a class of fully discrete dynamical systems, which have received considerable interest in various different areas. This brief addresses the observability of automata networks and switched automata networks in a unified framework, and proposes simple necessary and sufficient conditions for observability. The results are achieved by employing methods from symbolic computation, and are suited for implementation using computer algebra systems. Several examples are presented to demonstrate the application of the results.
关键词	Automata networks conjugacy observability recurrent neural networks switched systems
DOI	10.1109/TNNLS.2017.2651053
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000427859600053
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/29801
专题	系统科学研究所
通讯作者	Li, Rui
作者单位	1.Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China 2.Chinese Acad Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China 3.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China
推荐引用方式 GB/T 7714	Li, Rui,Hong, Yiguang,Wang, Xingyuan. Observability of Automata Networks: Fixed and Switching Cases[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(4):1388-1394.
APA	Li, Rui,Hong, Yiguang,&Wang, Xingyuan.(2018).Observability of Automata Networks: Fixed and Switching Cases.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(4),1388-1394.
MLA	Li, Rui,et al."Observability of Automata Networks: Fixed and Switching Cases".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.4(2018):1388-1394.