Stability and global Hopf bifurcation for neutral BAM neural network

Pseudo almost periodic solutions for CNNs with leakage delays and complex deviating arguments

“…It is well known that studies on neural dynamic systems not only involve a discussion of stability properties [1][2][3], but also involve many dynamic behaviors such as periodic oscillatory behavior [4,5], almost periodic oscillatory properties [6][7][8][9][10], chaos and bifurcation [11]. In the past four decades, people have paid much attention to the problem on almost periodic solutions and pseudo almost periodic solutions for cellular neural networks (CNNs) with leakage delays because of its successful applications in variety of areas such as signal processing, pattern recognition, chemical processes, nuclear reactors, biological systems, static image processing, associative memories, optimization problems and so on (see [12][13][14][15][16] and the references cited therein).…”

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.…”

Stability and Hopf Bifurcation Analysis of a General Tri-diagonal BAM Neural Network with Delays

“…Because the connection between the I layer and the J layer is complete, the existing results of the BAM neural network are very complicated and difficult to apply in practice. Inspired by many studies [11,[26][27][28][29][30][31][32][33][34][35][36], a general three-diagonal BAM system with 2n neurons and discrete delays will be proposed in this article, which is an adjacent connection, that is, the i-th The first (n + i − 1)-th, (n + i)-th and (n + i + 1) neurons in the I layer are only connected to the J layer Neurons in the (see Fig. 1).…”