期刊
NEUROCOMPUTING
卷 140, 期 -, 页码 1-7出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2013.12.054
关键词
Markovian jump systems; Neural networks; Discrete-time systems; Stochastic finite-time boundedness; Linear matrix inequalities
资金
- National Key Basic Research Program, China [2011CB710706, 2012CB215202]
- 111 Project [B12018]
- National Natural Science Foundation of China [61174058, 60974052, 61134001]
- Engineering and Physical Sciences Research Council, UK [EP/F029195]
- Plan of Nature Science Fundamental Research in Henan University of Technology [2012JCYJ13]
- Natural Science Foundation of Henan Province of China [132300410013, 132300410231]
This paper is concerned with stochastic finite-time boundedness analysis for a class of uncertain discrete-time neural networks with Markovian jump parameters and time-delays. The concepts of stochastic finite-time stability and stochastic finite-time boundedness are first given for neural networks. Then, applying the Lyapunov approach and the linear matrix inequality technique, sufficient criteria on stochastic finite-time boundedness are provided for the class of nominal or uncertain discrete-time neural networks with Markovian jump parameters and time-delays. It is shown that the derived conditions are characterized in terms of the solution to these linear matrix inequalities. Finally, numerical examples are included to illustrate the validity of the presented results. (C) 2014 Elsevier B.V. All rights reserved.
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