Exponential passivity of BAM neural networks with time-varying delays

Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities

“…Non-fragile synchronization for neural networks with time-varying delay and random controller gain fluctuations was studied in [9]. More results on stability, synchronization of various systems with impulsive effects has been found in [1,19,35,37] and some interesting results for BAM neural network models can be found in [8,15,24]. Despite the interesting results on memrister based neural networks and impulsive synchronization cited above, most of the existing works are mainly focused on neural networks like, Hopfield neural networks, Cohen-Grossberg type neural networks, cellular neural networks and some others with fixed impulse moments, whereas it is also of significant importance to study the memristor based BAM neural networks.…”

Exponential p-convergence analysis for stochastic BAM neural networks with time-varying and infinite distributed delays

“…In the existing literature [15,22,23], it always need that the value of activation function at zero is zero. However, here we do not need…”

“…The states of BAM neural networks are accomplished interactional between the two layers via both directions on associating, which can simulate the thinking mode of the human brain. Due to its special application, many researchers have studied existence and uniqueness of equilibrium point [2,3,[5][6][7][8][9][10][11][12][13][14][15][16][17] or the periodic solution [1,[18][19][20][21][22] and passivity [23,24] of BAM neural networks. But in many actual applications, these conclusions are no longer appropriate in the multistable dynamics [25,26], which have multiple equilibrium points and many of them are unstable.…”

Design disturbance attenuating controller for memristive recurrent neural networks with mixed time-varying delays

“…Therefore, since 2008, its potential applications have become more and more popular in many aspects such as generation computer, powerful brain-like neural computer, and so on. There is no doubt that it has initiated the worldwide concern with the emergence of the memristor (see [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]). For the neural networks, the first job is considering whether they are stable or not.…”