Dynamic event-triggered state estimation for discrete-time delayed switched neural networks with constrained bit rate

作者：Ran Zhang, Hongjian Liu, Yufei Liu, Hailong Tan · 发表于：Systems Science & Control Engineering · 年份：2024 · DOI：10.1080/21642583.2024.2334304 · 被引用次数：71 · 研究领域：Neural Networks Stability and Synchronization、Advanced Memory and Neural Computing、Machine Learning and ELM

In this paper, a class of discrete-time delayed switched neural networks with dynamic event-triggered mechanism (DETM) and constrained bit rate is considered. In order to reduce the transmission frequency and alleviate the unnecessary resource loss between sensor and estimator, a DETM is proposed. The data transmission from sensor to estimator is realized through constrained bit rate channel. Therefore, in order to reflect the bandwidth allocation rules of accessible neurone nodes, a bit rate constraint model is introduced and an encoding-decoding mechanism is developed. This paper is concerned with the strategy of average dwell time (ADT) and linear matrix inequality, then sufficient conditions for the exponential ultimate boundedness of switched neural networks with DETM and constrained bit rate are proposed. Finally, an example is given to prove the effectiveness of the results.