The stochastic stability problem was considered for a class of gene regulatory networks with mixed time-delays.The mixed time-delays under consideration comprise both discrete timevarying delays and distributed time-d...The stochastic stability problem was considered for a class of gene regulatory networks with mixed time-delays.The mixed time-delays under consideration comprise both discrete timevarying delays and distributed time-delays.By employing a new Lyapunov function and conducting stochastic analysis,a linear matrix inequality(LMI) approach was developed to derive the criteria ensuring stability.The proposed criteria can be checked by using Matlab LMI toolbox.A simple example was provided to demonstrate the good effectiveness and applicability of the proposed testing criteria.展开更多
This paper studies the phenomenon of stochastic resonance in an asymmetric bistable system with time-delayed feedback and mixed periodic signal by using the theory of signal-to-noise ratio in the adiabatic limit. A ge...This paper studies the phenomenon of stochastic resonance in an asymmetric bistable system with time-delayed feedback and mixed periodic signal by using the theory of signal-to-noise ratio in the adiabatic limit. A general approximate Fokker-Planck equation and the expression of the signal-to-noise ratio are derived through the small time delay approximation at both fundamental harmonics and mixed harmonics. The effects of the additive noise intensity Q, multiplicative noise intensity D, static asymmetry r and delay time T on the signal-to-noise ratio are discussed. It is found that the higher mixed harmonics and the static asymmetry r can restrain stochastic resonance, and the delay time τ can enhance stochastic resonance. Moreover, the longer the delay time τ is, the larger the additive noise intensity Q and the multiplicative noise intensity D are, when the stochastic resonance appears.展开更多
In this paper, the stability in Lagrange sense of a class of stochastic static neural networks with mixed time delays is studied. Based on the Lyapunov stability theory and with the help of stochastic analysis techniq...In this paper, the stability in Lagrange sense of a class of stochastic static neural networks with mixed time delays is studied. Based on the Lyapunov stability theory and with the help of stochastic analysis technique, the criteria for the stability in Lagrange sense of stochastic static neural networks with mixed time delays is obtained. One example is given to verify the advantage and applicability of the proposed results.展开更多
基金National Natural Science Foundation of China (No. 60874113)Key Creative Project of Shanghai Education Community,China (No. 09ZZ66)+1 种基金the Research Fund for the Doctoral Program of Higher Education,China (No. 200802550007)Key Basic Research Project of Shanghai,China (No. 09JC1400700)
文摘The stochastic stability problem was considered for a class of gene regulatory networks with mixed time-delays.The mixed time-delays under consideration comprise both discrete timevarying delays and distributed time-delays.By employing a new Lyapunov function and conducting stochastic analysis,a linear matrix inequality(LMI) approach was developed to derive the criteria ensuring stability.The proposed criteria can be checked by using Matlab LMI toolbox.A simple example was provided to demonstrate the good effectiveness and applicability of the proposed testing criteria.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 10872165 and 10902085)
文摘This paper studies the phenomenon of stochastic resonance in an asymmetric bistable system with time-delayed feedback and mixed periodic signal by using the theory of signal-to-noise ratio in the adiabatic limit. A general approximate Fokker-Planck equation and the expression of the signal-to-noise ratio are derived through the small time delay approximation at both fundamental harmonics and mixed harmonics. The effects of the additive noise intensity Q, multiplicative noise intensity D, static asymmetry r and delay time T on the signal-to-noise ratio are discussed. It is found that the higher mixed harmonics and the static asymmetry r can restrain stochastic resonance, and the delay time τ can enhance stochastic resonance. Moreover, the longer the delay time τ is, the larger the additive noise intensity Q and the multiplicative noise intensity D are, when the stochastic resonance appears.
基金supported by the National Natural Science Foundation of China(11171374)Natural Science Foundation of Shandong Province(ZR2011AZ001)
文摘In this paper, the stability in Lagrange sense of a class of stochastic static neural networks with mixed time delays is studied. Based on the Lyapunov stability theory and with the help of stochastic analysis technique, the criteria for the stability in Lagrange sense of stochastic static neural networks with mixed time delays is obtained. One example is given to verify the advantage and applicability of the proposed results.
