6533b827fe1ef96bd128663c

RESEARCH PRODUCT

Finite-time boundedness for uncertain discrete neural networks with time-delays and Markovian jumps

Sing Kiong NguangJianhua ZhangHamid Reza KarimiYingqi ZhangPeng ShiPeng Shi

subject

Lyapunov functionDiscrete-time systems; Linear matrix inequalities; Markovian jump systems; Neural networks; Stochastic finite-time boundedness; Artificial Intelligence; Computer Science Applications1707 Computer Vision and Pattern Recognition; Cognitive NeuroscienceArtificial neural networkMarkov chainStochastic processCognitive NeuroscienceMarkovian jump systemsLinear matrix inequalitiesLinear matrix inequalityComputer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science Applicationssymbols.namesakeDiscrete time and continuous timeArtificial IntelligenceDiscrete-time systemssymbolsCalculusApplied mathematicsStochastic neural networkJump processNeural networksStochastic finite-time boundednessMathematics

description

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. Refereed/Peer-reviewed

yearjournalcountryeditionlanguage
2014-09-01
10.1016/j.neucom.2013.12.054https://hdl.handle.net/11541.2/120706