Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements
Shen, Bo1; Wang, Zidong2; Qiao, Hong3,4; Hong Qiao
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2017-05-01
Volume28Issue:5Pages:1152-1163
SubtypeArticle
Abstract
; In this paper, the event-triggered state estimation problem is investigated for a class of discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. In order to cater for more realistic transmission process of the neural signals, we make the first attempt to introduce a set of stochastic variables to characterize the random fluctuations of system parameters. In the addressed neural network model, the delays among the interconnections are allowed to be different, which are more general than those in the existing literature. The incomplete information under consideration includes randomly occurring sensor saturations and quantizations. For the purpose of energy saving, an event-triggered state estimator is constructed and a sufficient condition is given under which the estimation error dynamics is exponentially ultimately bounded in the mean square. It is worth noting that the ultimate boundedness of the error dynamics is explicitly estimated. The characterization of the desired estimator gain is designed in terms of the solution to a certain matrix inequality. Finally, a numerical simulation example is presented to illustrate the effectiveness of the proposed event-triggered state estimation scheme.
KeywordEvent-triggered State Estimation Exponentially Ultimate Boundedness Incomplete Measurements Neural Networks Quantizations Sensor Saturations Stochastic Parameters
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2016.2516030
WOS KeywordRANDOMLY OCCURRING NONLINEARITIES ; EXPONENTIAL STABILITY ANALYSIS ; INFINITE DISTRIBUTED DELAYS ; MULTIAGENT SYSTEMS ; OUTPUT-FEEDBACK ; ROBOTIC MANIPULATORS ; CONSENSUS CONTROL ; COMPLEX NETWORKS ; SYNCHRONIZATION ; MODE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61473076 ; Shu Guang project of Shanghai Municipal Education Commission ; Shanghai Education Development Foundation(13SG34) ; Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning ; Fundamental Research Funds for the Central Universities ; DHU Distinguished Young Professor Program ; 61329301 ; 61210009 ; 61134009)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000401981800011
Citation statistics
Cited Times:61[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12616
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorHong Qiao
Affiliation1.Donghua Univ, Sch Informat Sci & Technol, Shanghai 201620, Peoples R China
2.Brunel Univ, Dept Comp Sci, London UB8 3PH, Uxbridge, England
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
Recommended Citation
GB/T 7714
Shen, Bo,Wang, Zidong,Qiao, Hong,et al. Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(5):1152-1163.
APA Shen, Bo,Wang, Zidong,Qiao, Hong,&Hong Qiao.(2017).Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(5),1152-1163.
MLA Shen, Bo,et al."Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.5(2017):1152-1163.
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