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In this paper, we study global robust asymptotic stability of the equilibrium point for neural networks with multiple time delays. By employing suitable Lyapunov functionals, we derive a set of delay independent sufficient conditions for global robust asymptotic stability of this class of neural networks.
Object counting has been used in many areas such as medical and industrial applications. It is a challenging problem to count the target objects in high speed. It is useful to implement image processing applications using the high capability computational power offered by Cellular Neural Network type analog processor named as ACE16k. In this paper, we implement an efficient object counting algorithm...
This paper studies the global convergence properties of continuous-time neural networks with multiple time delays. By employing suitable and more general Lyapunov functionals, we derive a new delay independent sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point. The results are applicable to all continuous non-monotonic neuron activation functions...
Global robust convergence properties of continuous-time neural networks with discrete delays are studied. By employing suitable Lyapunov functionals, we derive a set of delay independent sufficient conditions for the existence, uniqueness and global robust asymptotic stability of the equilibrium point. The conditions can be easily verified as they can be expressed in terms of the network parameters...
This paper presents a sufficient condition for the existence, uniqueness and global robust asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with discrete time delays. Some numerical examples are given to compare our results with the previous robust stability results derived in the literature.
This correspondence presents a sufficient condition for the existence, uniqueness, and global robust asymptotic stability of the equilibrium point for bidirectional associative memory neural networks with discrete time delays. The results impose constraint conditions on the network parameters of the neural system independently of the delay parameter, and they are applicable to all bounded continuous...
This paper studies the stability properties of a more general class of bidirectional associative memory (BAM) neural networks with constant time delays. Without assuming the symmetry of the interconnection matrices, and monotonicity and differentiability of the activation functions, we derive a new sufficient condition for the global asymptotic stability of the equilibrium point for bidirectional...
This paper presents new sufficient conditions for the existence of stable equilibrium points in the total saturation region for delayed cellular neural networks (DCNNs). The obtained results are compared with the previous results derived in the literature. It is also shown that the results of this paper generalize the previous literature results for the existence of stable equilibrium points for DCNNs...
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