Protocol-Based Non-Fragile State Estimation for Delayed Recurrent Neural Networks Subject to Replay Attacks

• Published in: IEEE/CAA journal of automatica sinica Vol. 11; no. 1; pp. 249 - 251
• Main Authors: Yang, Fan, Dong, Hongli, Shen, Yuxuan, Li, Xuerong, Dai, Dongyan
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Artificial Intelligence Energy Research Institute,Northeast Petroleum University,Daqing 163318,the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control,Northeast Petroleum University,Daqing 163318,and the Sanya Offshore Oil & Gas Research Institute,Sanya 572025,China
• Subjects: Communication networks; Recurrent neural networks; State estimation
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2023.123936

Dear Editor, This letter focuses on the protocol-based non-fragile state estimation problem for a class of recurrent neural networks (RNNs). With the development of communication technology, the networked systems have received particular attentions. The networked system brings advantages such as easy to implement, high flexibility as well as low cost, and also has disadvantages such as limited bandwidth of the communication network which lead to networked-induced phenomena [1], [2]. To alleviate the network-induced phenomena, communication protocols have been introduced in the communication networks of the networked systems [3], [4]. As a widely used communication protocol in real practice, the round-robin (RR) protocol has received research interest and the state estimation problem under the RR protocol is an on-going hotspot in the area of signal processing [5]. Nevertheless, for the RNNs, the corresponding RR protocol-based state estimation problem still needs further research effort which is the first motivation of this letter.