Enhanced robust finite-time passivity for Markovian jumping discrete-time BAM neural networks with leakage delay

Advances in Difference Equations

C Sowmiya¹  G Rajchakit²  Ahmed Alsaedi³  R Raja⁴  Jinde Cao⁵ 

[1] Department of Mathematics, Alagappa University, Karaikudi, India;Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai, Thailand;Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, King Abdulaziz University, Jeddah, Saudi Arabia;Ramanujan Centre for Higher Mathematics, Alagappa University, Karaikudi, India;School of Mathematics, Southeast University, Nanjing, China

关键词: LMIs;    Markovian jumping systems;    leakage delay;    bidirectional associative memory;    discrete-time neural networks;    passivity and stability analysis;   

DOI  :  10.1186/s13662-017-1378-9

学科分类：数学（综合）

来源: SpringerOpen

【 摘 要 】

This paper is concerned with the problem of enhanced results on robust finite-time passivity for uncertain discrete-time Markovian jumping BAM delayed neural networks with leakage delay. By implementing a proper Lyapunov-Krasovskii functional candidate, the reciprocally convex combination method together with linear matrix inequality technique, several sufficient conditions are derived for varying the passivity of discrete-time BAM neural networks. An important feature presented in our paper is that we utilize the reciprocally convex combination lemma in the main section and the relevance of that lemma arises from the derivation of stability by using Jensen’s inequality. Further, the zero inequalities help to propose the sufficient conditions for finite-time boundedness and passivity for uncertainties. Finally, the enhancement of the feasible region of the proposed criteria is shown via numerical examples with simulation to illustrate the applicability and usefulness of the proposed method.

【 授权许可】

CC BY