The exponential stability problem is investigated for a class of stochastic recurrent neural networks with time delay and Markovian switching. By using Ito＇s differential formula and the Lyapunov stability theory, su...The exponential stability problem is investigated for a class of stochastic recurrent neural networks with time delay and Markovian switching. By using Ito＇s differential formula and the Lyapunov stability theory, sufficient condition for the solvability of this problem is derived in term of linear matrix inequalities, which can be easily checked by resorting to available software packages. A numerical example and the simulation are exploited to demonstrate the effectiveness of the proposed results.展开更多
To establish easily proved conditions under which the random delayed recurrent neural network with Markovian switching is mean-square stability,the evolution of the delay was modeled by a continuous-time homogeneous M...To establish easily proved conditions under which the random delayed recurrent neural network with Markovian switching is mean-square stability,the evolution of the delay was modeled by a continuous-time homogeneous Markov process with a finite number of states.By employing Lyapunov-Krasovskii functionals and conducting stochastic analysis,a linear matrix inequality (LMI) approach was developed to derive the criteria for mean-square stability,which can be readily checked by some standard numerical packages such as the Matlab LMI Toolbox.A numerical example was exploited to show the usefulness of the derived LMI-based stability conditions.展开更多
文摘The exponential stability problem is investigated for a class of stochastic recurrent neural networks with time delay and Markovian switching. By using Ito＇s differential formula and the Lyapunov stability theory, sufficient condition for the solvability of this problem is derived in term of linear matrix inequalities, which can be easily checked by resorting to available software packages. A numerical example and the simulation are exploited to demonstrate the effectiveness of the proposed results.
基金Sponsored by the National Natural Science Foundation of China(Grant No.10771044))the Natural Science Foundation of Heilongjiang Province(GrantNo.200605)+1 种基金the Excellent Youth Foundation of Educational Committee of Hunan provincial(Grant No.08B005)the Scientific Research Funds of Hunan Provincial Education Department of China(Grant No.08C119)
文摘To establish easily proved conditions under which the random delayed recurrent neural network with Markovian switching is mean-square stability,the evolution of the delay was modeled by a continuous-time homogeneous Markov process with a finite number of states.By employing Lyapunov-Krasovskii functionals and conducting stochastic analysis,a linear matrix inequality (LMI) approach was developed to derive the criteria for mean-square stability,which can be readily checked by some standard numerical packages such as the Matlab LMI Toolbox.A numerical example was exploited to show the usefulness of the derived LMI-based stability conditions.
