Event-triggered state estimation for Markovian jumping neural networks: On mode-dependent delays and uncertain transition probabilities

NEUROCOMPUTING

卷:424

Article

Yang, Hua¹  Wang, Zidong²  Shen, Yuxuan^3,4  Alsaadi, Fuad E.⁵  Alsaadi, Fawaz E.⁶ 

[1] Shanxi Agr Univ, Coll Informat Sci & Engn, Taigu 030801, Shanxi, Peoples R China

[2] Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England

[3] Northeast Petr Univ, Artificial Intelligence Energy Res Inst, Daqing 163318, Peoples R China

[4] Northeast Petr Univ, Heilongjiang Prov Key Lab Networking & Intelligen, Daqing 163318, Peoples R China

[5] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia

[6] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Technol, Jeddah 21589, Saudi Arabia

关键词: Artificial neural networks;    Markovian jumping parameters;    Uncertain transition probabilities;    Event-triggered mechanism;    Mode-dependent time-delays;   

DOI  :  10.1016/j.neucom.2020.10.050

来源: Elsevier

【 摘 要 】

This paper is concerned with the event-triggered state estimation (ETSE) problem for a class of discrete time Markovian jumping neural networks with mode-dependent time-delays and uncertain transition probabilities. The parameters of the neural networks experience switches that are characterized by a Markovian chain whose transition probabilities are allowed to be uncertain. The event-triggered mechanism is introduced in the sensor-to-estimator channel to reduce the frequency of signal communication. The aim of this paper is to develop an ETSE scheme such that the estimation error dynamics is exponentially ultimately bounded in the mean square. To achieve the aim, two sufficient conditions are proposed with the first one guaranteeing the existence of the required state estimator, and the second one giving the algorithm for designing the corresponding estimator gain by solving some matrix inequalities. In the end, the validity of the proposed estimation scheme is illustrated by a numerical example. (c) 2020 Elsevier B.V. All rights reserved.

【 授权许可】

Free