Conditions for complete and lag synchronizations in drive-response systems are considered under the unified framework of generalized synchronization. The question is addressed that whether the synchronization conditio...Conditions for complete and lag synchronizations in drive-response systems are considered under the unified framework of generalized synchronization. The question is addressed that whether the synchronization conditions achieving complete synchronization is still valid for lag synchronization when the time delay of signal transmission between the drive and response systems increases from 0. Theoretical and numerical results show that whether the synchronization conditions is stable for the influence of the time delay of signal transmission depends on a particular form of equilibria of the drive and response systems. Furthermore, it seems that the less the number of the equilibria of the drive system, the more likely the synchronization conditions are stable for the time delay of signal trans- mission.展开更多
The phenomena of GS in a drive-response system have been studied. Several kinds of GS are found： the driver-induced GS, the responser-induced GS, and the intermediate GS. The mechanism generating these types of GS is...The phenomena of GS in a drive-response system have been studied. Several kinds of GS are found： the driver-induced GS, the responser-induced GS, and the intermediate GS. The mechanism generating these types of GS is given and the roles played by response and drive dynamics in realizing different GS states are discussed.展开更多
This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model,which is the interconnection of a linear delayed dynamic syste...This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model,which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator,and covers several well-known neural networks,such as Hopfield neural networks,cellular neural networks(CNNs),bidirectional associative memory(BAM)networks,recurrent multilayer perceptrons(RMLPs).By virtue of Lyapunov-Krasovskii stability theory and linear matrix inequality(LMI)technique,some exponential synchronization criteria are derived.Using the drive-response concept,hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria.Finally,detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.展开更多
This paper first investigates the projective synchronisation problem with non-delayed and delayed coupling between drive-response dynamical networks consisting of identical nodes and different nodes. Based on Lyapunov...This paper first investigates the projective synchronisation problem with non-delayed and delayed coupling between drive-response dynamical networks consisting of identical nodes and different nodes. Based on Lyapunov stability theory, several nonlinear controllers are applied to achieve the projective synchronisation between the drive-response dynamical networks; simultaneously the topological structure of the drive dynamical complex networks can be exactly identified. Moreover, numerical examples are presented to verify the feasibility and effectiveness of the theorems.展开更多
This paper studies the generalized synchronization of a class of drive-response neural networks with time-varying delay. When the topological structures of the drive-response neural networks are known, by designing an...This paper studies the generalized synchronization of a class of drive-response neural networks with time-varying delay. When the topological structures of the drive-response neural networks are known, by designing an appropriate nonlinear adaptive controller, the generalized synchronization of these two networks is obtained based on Lyapunov stability theory and LaSalle’s invariance principle.展开更多
基金supported by the National Natural Science Foundation of China(11002103 and 11032009)Shanghai Leading Academic Discipline(B302)
文摘Conditions for complete and lag synchronizations in drive-response systems are considered under the unified framework of generalized synchronization. The question is addressed that whether the synchronization conditions achieving complete synchronization is still valid for lag synchronization when the time delay of signal transmission between the drive and response systems increases from 0. Theoretical and numerical results show that whether the synchronization conditions is stable for the influence of the time delay of signal transmission depends on a particular form of equilibria of the drive and response systems. Furthermore, it seems that the less the number of the equilibria of the drive system, the more likely the synchronization conditions are stable for the time delay of signal trans- mission.
基金National Natural Science Foundation of China under Grant No.10405004
文摘The phenomena of GS in a drive-response system have been studied. Several kinds of GS are found： the driver-induced GS, the responser-induced GS, and the intermediate GS. The mechanism generating these types of GS is given and the roles played by response and drive dynamics in realizing different GS states are discussed.
基金Project supported in part by the National Natural Science Foundationof China (No. 60504024)the Specialized Research Fund for theDoctoral Program of Higher Education,China (No. 20060335022)+1 种基金theNatural Science Foundation of Zhejiang Province (No. Y106010),China the "151 Talent Project" of Zhejiang Province (Nos.05-3-1013 and 06-2-034),China
文摘This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model,which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator,and covers several well-known neural networks,such as Hopfield neural networks,cellular neural networks(CNNs),bidirectional associative memory(BAM)networks,recurrent multilayer perceptrons(RMLPs).By virtue of Lyapunov-Krasovskii stability theory and linear matrix inequality(LMI)technique,some exponential synchronization criteria are derived.Using the drive-response concept,hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria.Finally,detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.
基金supported by the National Natural Science Foundation of China (Grant No. 10771088)Natural Science Foundation of Jiangsu Province,China (Grant No. 2007098)+3 种基金Outstanding Personnel Program in Six Fields of Jiangsu Province,China (Grant No. 6-A-029)National Natural Science (Youth) Foundation of China (Grant No. 10801140)Youth Foundation of Chongqing Normal University,China (Grant No. 08XLQ04)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. CX09B 202Z)
文摘This paper first investigates the projective synchronisation problem with non-delayed and delayed coupling between drive-response dynamical networks consisting of identical nodes and different nodes. Based on Lyapunov stability theory, several nonlinear controllers are applied to achieve the projective synchronisation between the drive-response dynamical networks; simultaneously the topological structure of the drive dynamical complex networks can be exactly identified. Moreover, numerical examples are presented to verify the feasibility and effectiveness of the theorems.
文摘This paper studies the generalized synchronization of a class of drive-response neural networks with time-varying delay. When the topological structures of the drive-response neural networks are known, by designing an appropriate nonlinear adaptive controller, the generalized synchronization of these two networks is obtained based on Lyapunov stability theory and LaSalle’s invariance principle.
