This paper is devoted to the investigation of stability for a class of coupled impulsive Markovian jump reaction-diffusion systems on networks(CIMJRDSNs). By using graph theory, a systematic method is provided to cons...This paper is devoted to the investigation of stability for a class of coupled impulsive Markovian jump reaction-diffusion systems on networks(CIMJRDSNs). By using graph theory, a systematic method is provided to construct global Lyapunov functions for the CIMJRDSNs. Based on Lyapunov functions and stochastic analysis method, some novel stability principles associated with the topology property of the networks are established.展开更多
This paper is devoted to investigating the dynamic output feedback(DOF)control problem of Markovian jump neutral-type stochastic systems with a guaranteed cost function.Both of the state and measurement equations cont...This paper is devoted to investigating the dynamic output feedback(DOF)control problem of Markovian jump neutral-type stochastic systems with a guaranteed cost function.Both of the state and measurement equations contain time delays.Mode-dependent DOF controllers are first designed such that the closed-loop system is asymptotically stable in mean-square and an adequate performance level of this system is guaranteed.Then,sufficient conditions for the solvability of this problem are derived in the form of linear matrix inequalities(LMIs).A numerical example is presented to reveal the effectiveness of our findings.展开更多
In this paper, we implement topological degree theory and Lyapunov-functional methods to obtain the existence and uniqueness of the equilibrium point and its global robust stability for interval Hopfield neural networ...In this paper, we implement topological degree theory and Lyapunov-functional methods to obtain the existence and uniqueness of the equilibrium point and its global robust stability for interval Hopfield neural networks with continuously distributed delays. Moreover, the methods used in judging the robust stability are proven practical and easily verifiable.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61473097,11301090the State Key Program of Natural Science Foundation of China under Grant No.U1533202+2 种基金Shandong Independent Innovation and Achievements Transformation Fund under Grant No.2014CGZH1101Civil Aviation Administration of China under Grant No.MHRD20150104Guangxi Natural Science Foundation under Grant No.2016JJA110005
文摘This paper is devoted to the investigation of stability for a class of coupled impulsive Markovian jump reaction-diffusion systems on networks(CIMJRDSNs). By using graph theory, a systematic method is provided to construct global Lyapunov functions for the CIMJRDSNs. Based on Lyapunov functions and stochastic analysis method, some novel stability principles associated with the topology property of the networks are established.
基金supported by the National Natural Science Foundation of China under Grant Nos.61703226and 71961002Startup Project of Doctor Scientific Research of Guangxi University of Finance and Economics BS 2019002。
文摘This paper is devoted to investigating the dynamic output feedback(DOF)control problem of Markovian jump neutral-type stochastic systems with a guaranteed cost function.Both of the state and measurement equations contain time delays.Mode-dependent DOF controllers are first designed such that the closed-loop system is asymptotically stable in mean-square and an adequate performance level of this system is guaranteed.Then,sufficient conditions for the solvability of this problem are derived in the form of linear matrix inequalities(LMIs).A numerical example is presented to reveal the effectiveness of our findings.
基金the National Natural Science Foundation of China under grant 60674020the Natural Science Foundation of Shandong under grant Z2006G11
文摘In this paper, we implement topological degree theory and Lyapunov-functional methods to obtain the existence and uniqueness of the equilibrium point and its global robust stability for interval Hopfield neural networks with continuously distributed delays. Moreover, the methods used in judging the robust stability are proven practical and easily verifiable.
