期刊
NEUROCOMPUTING
卷 174, 期 -, 页码 912-920出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2015.10.017
关键词
Discrete-time genetic regulatory networks; Event-triggered state estimation; H-infinity state estimation; Markovian jumping parameters; Stochastic perturbations; Time-varying delays
资金
- National Natural Science Foundation of China [61473076, 61374010]
- 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
In this paper, the event-triggered H-infinity state estimation problem is investigated for a class of discrete-time stochastic genetic regulatory networks with both Markovian jumping parameters and time-varying delays. The jumping parameters are governed by a homogeneous Markovian chain and the time-varying delays under consideration occur in both the feedback regulatory process and transcription process. The aim of this paper is to estimate the concentrations of mRNA and protein in such genetic regulatory networks by using the available measurement outputs. In order to reduce the information communication burden,, the event-triggered mechanism is adopted and the measurement outputs are only transmitted to the estimator when a certain triggered condition is met. By constructing an appropriate Lyapunov functional, some sufficient conditions are derived under which the estimation error dynamics is stochastically stable and the H-infinity performance constraint is satisfied. Based on the analysis results, the desired H-infinity estimator parameters are designed in terms of the solution to a set of matrix inequalities that can be easily solved by the Matlab toolboxes. Finally, a simulation example is provided to illustrate the effectiveness of the proposed event-triggered state estimation scheme. (C) 2015 Elsevier B.V. All rights reserved.
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