Adaptive H∞ synchronization of chaotic systems via linear and nonlinear feedback control is investigated. The chaotic systems are redesigned by using the generalized Hamiltonian systems and observer approach. Based o...Adaptive H∞ synchronization of chaotic systems via linear and nonlinear feedback control is investigated. The chaotic systems are redesigned by using the generalized Hamiltonian systems and observer approach. Based on Lya-punov＇s stability theory, linear and nonlinear feedback control of adaptive H∞ synchronization is established in order to not only guarantee stable synchronization of both master and slave systems but also reduce the effect of external disturbance on an Hoe-norm constraint. Adaptive H∞ synchronization of chaotic systems via three kinds of control is investigated with applications to Lorenz and Chen systems. Numerical simulations are also given to identify the effectiveness of the theoretical analysis.展开更多
Information encoding plays a crucial role in neuroscience. One of the fundamental questions in cognitive ncu- roscienee is how the brain encodes external stimuli in the sensory cortex. We use a network model based on ...Information encoding plays a crucial role in neuroscience. One of the fundamental questions in cognitive ncu- roscienee is how the brain encodes external stimuli in the sensory cortex. We use a network model based on the Hodgkin-Huxley type to study the information transmitting including its storage and recall. The model is inspired by psychological and neurobiological evidence on sequential memories. The model contains excitatory and inhibitory neurons with all-to-all connections whose architecture has two layers. A lower layer represents consecutive events during the information encoding process, and the upper layer is used to tag sequences of events represented in the lower layer. The spike-timing-dependent plasticity learning rule is used for sequential storage of excitatory connections between the modules. Computer simulations demonstrate that the synchronization status of multiple neurons is dependent on the network connectivity patterns, and also this model has good performance for different sequences of storage and recall.展开更多
文摘Adaptive H∞ synchronization of chaotic systems via linear and nonlinear feedback control is investigated. The chaotic systems are redesigned by using the generalized Hamiltonian systems and observer approach. Based on Lya-punov＇s stability theory, linear and nonlinear feedback control of adaptive H∞ synchronization is established in order to not only guarantee stable synchronization of both master and slave systems but also reduce the effect of external disturbance on an Hoe-norm constraint. Adaptive H∞ synchronization of chaotic systems via three kinds of control is investigated with applications to Lorenz and Chen systems. Numerical simulations are also given to identify the effectiveness of the theoretical analysis.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11672107,11402294,11602224 and 11502062
文摘Information encoding plays a crucial role in neuroscience. One of the fundamental questions in cognitive ncu- roscienee is how the brain encodes external stimuli in the sensory cortex. We use a network model based on the Hodgkin-Huxley type to study the information transmitting including its storage and recall. The model is inspired by psychological and neurobiological evidence on sequential memories. The model contains excitatory and inhibitory neurons with all-to-all connections whose architecture has two layers. A lower layer represents consecutive events during the information encoding process, and the upper layer is used to tag sequences of events represented in the lower layer. The spike-timing-dependent plasticity learning rule is used for sequential storage of excitatory connections between the modules. Computer simulations demonstrate that the synchronization status of multiple neurons is dependent on the network connectivity patterns, and also this model has good performance for different sequences of storage and recall.
