Novel robust stability criteria for stochastic hopfield neural networks with time delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New results for robust stability of dynamical neural networks with discrete time delays
Expert Systems with Applications: An International Journal
On the transient and steady-state estimates of interval genetic regulatory networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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 nonmonotonic neuron activation functions. Some numerical examples are given to compare our results with the previous robust stability results derived in the literature.