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In this paper, a hybrid control strategy, variable universe adaptive fuzzy sliding mode control, is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as...
Nowadays congestion control problem of the intermediate nodes in the Internet has received extensively attention in networking and control community. In this paper, a novel adaptive PID (Proportional-Integral-Differential) controller based on neural networks for the problem of AQM with ECN marks is presented. Considering a previously developed nonlinear dynamic model of TCP/AQM system and the queue...
This paper presents a computational model of neural network for both spatial and temporal weights, and a unified adaptation scheme based on two biologically plausible learning rules-Hebbian rule and lateral inhibition is proposed. This model is applied to color video environment to develop a set of complete spatiotemporal weights simulating receptive field of simple cell in primary visual cortex,...
Eye movement plays an important role in human vision system. How to control eye or gaze movement automatically for image understanding is an interesting issue. This paper presents a progress of our research on biological-inspired computational modeling of eye-motion control for object detection in images. The model simulates the single and population cell coding mechanisms for learning visual context...
Image motion due to self motion is an important cue biological systems use for gathering information about the environment. The motion energy model is commonly used to model the responses of motion selective neurons in the mammalian primary visual cortex. Here, we investigate the hypothesis that these low level responses are directly useful for navigation. This avoids the need for estimating a model...
Point process modeling has the potential to capture the specificity of neural firing where the information is contained in the spike time occurrence. We aim at building an adaptive signal processing framework for brain machine interfaces working directly in the spike domain. However, the signal processing tools for continuous stochastic processes faces challenge when implemented directly on point...
In this paper, dropped the assumption of the boundedness of the activation functions, the global dynamics are investigated for the recurrently connected neural networks (RCNNs) with discontinuous activations and time-varying delays. Based on the nonsmooth analysis theory, linear matrix inequality (LMI) technique and differential inclusions approach, several sufficient conditions are obtained to ensure...
This paper discusses the synthesis problem for a class of discrete time complex-valued Hopfield neural network. To be an associative memory, each memory pattern of the network should be stable and attractive. For this reason, this paper firstly analysis the stability of network, where a generalized Hamming distance defined in complex-valued domain is used to be Lyapunov function. Thus a stable criterion...
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