This paper studies the general decay synchronization(GDS)of a class of recurrent neural networks(RNNs)with general activation functions and mixed time delays.By constructing suitable Lyapunov-Krasovskii functionals an...This paper studies the general decay synchronization(GDS)of a class of recurrent neural networks(RNNs)with general activation functions and mixed time delays.By constructing suitable Lyapunov-Krasovskii functionals and employing useful inequality techniques,some sufficient conditions on the GDS of considered RNNs are established via a type of nonlinear control.In addition,one example with numerical simulations is presented to illustrate the obtained theoretical results.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supported by the National Natural Science Foundation of Xinjiang under Grant No.2016D01C075。
文摘This paper studies the general decay synchronization(GDS)of a class of recurrent neural networks(RNNs)with general activation functions and mixed time delays.By constructing suitable Lyapunov-Krasovskii functionals and employing useful inequality techniques,some sufficient conditions on the GDS of considered RNNs are established via a type of nonlinear control.In addition,one example with numerical simulations is presented to illustrate the obtained theoretical results.
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.
