This paper proposes a nonlinear feedback control method to realize global exponential synchronization with channel time-delay between the Lfi system and Chen system, which are regarded as the drive system and the resp...This paper proposes a nonlinear feedback control method to realize global exponential synchronization with channel time-delay between the Lfi system and Chen system, which are regarded as the drive system and the response system respectiveiy. Some effective observers are produced to identify the unknown parameters of the Lii system. Based on the Lyapunov stability theory and linear matrix inequality technique, some sufficient conditions of global exponential synchronization of the two chaotic systems are derived. Simulation results show the effectiveness and feasibility of the proposed controller.展开更多
This paper proposes an y2-y∞ learning law as a new learning method for dynamic neural networks with external disturbance. Based on linear matrix inequality （LMI） formulation, the y2-y∞ learning law is presented to...This paper proposes an y2-y∞ learning law as a new learning method for dynamic neural networks with external disturbance. Based on linear matrix inequality （LMI） formulation, the y2-y∞ learning law is presented to not only guarantee asymptotical stability of dynamic neural networks but also reduce the effect of external disturbance to an y2-y∞ induced norm constraint. It is shown that the design of the y2-y∞ learning law for such neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning law.展开更多
By employing the Lyapunov stability theory and linear matrix inequality（LMI）technique,delay-dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory（BAM）neu...By employing the Lyapunov stability theory and linear matrix inequality（LMI）technique,delay-dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory（BAM）neural networks with time-varying delays.The proposed condition can be checked easily by LMI control toolbox in Matlab.A numerical example is given to demonstrate the effectiveness of our results.展开更多
基金Project supported by the Fundamental Research Funds for the Central Universities (Grant No. CDJZR10 17 00 02)
文摘This paper proposes a nonlinear feedback control method to realize global exponential synchronization with channel time-delay between the Lfi system and Chen system, which are regarded as the drive system and the response system respectiveiy. Some effective observers are produced to identify the unknown parameters of the Lii system. Based on the Lyapunov stability theory and linear matrix inequality technique, some sufficient conditions of global exponential synchronization of the two chaotic systems are derived. Simulation results show the effectiveness and feasibility of the proposed controller.
基金Project supported by the Grant of the Korean Ministry of Education, Science and Technology (The Regional Core Research Program/Center for Healthcare Technology Development)
文摘This paper proposes an y2-y∞ learning law as a new learning method for dynamic neural networks with external disturbance. Based on linear matrix inequality （LMI） formulation, the y2-y∞ learning law is presented to not only guarantee asymptotical stability of dynamic neural networks but also reduce the effect of external disturbance to an y2-y∞ induced norm constraint. It is shown that the design of the y2-y∞ learning law for such neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning law.
基金supported by Natural Science Foundation of Hebei Province under Grant No.E2007000381
文摘By employing the Lyapunov stability theory and linear matrix inequality（LMI）technique,delay-dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory（BAM）neural networks with time-varying delays.The proposed condition can be checked easily by LMI control toolbox in Matlab.A numerical example is given to demonstrate the effectiveness of our results.
