Fixed/Preassigned-time synchronization of quaternion-valued neural networks via pure power-law control

Weight learning for ℋ∞$$ {\mathscr{H}}_{\infty } $$ stabilization of uncertain switched neural networks with external disturbance and reaction‐diffusion

“…Theorems 1 and 2 are based on the Dirichlet boundary condition. When the boundary condition is changed to the Neumann type, the weight learning rule (10) can also be used to ensure the  ∞ stability of SNN (5). However, since Lemma 2 does not hold under the the Neumann boundary condition, (16) needs to be modified to…”

“…Over the last several decades, various dynamic neural networks models, including classical Hopfield networks, 1 switched neural networks (SNNs), 2 stochastic neural networks, 3 competitive neural networks, 4 quaternion‐valued neural networks, 5 fractional neural networks, 6,7 neutral‐type delay neural networks, 8 interval neural networks, 9 memristor‐based neural networks, 10,11 etc., have been constructed and applied successfully to different types of fields 12,13 . Among these models, SNNs, with the development of the understanding of the practical significance of hybrid systems, have gained increasing research interest from the physics and engineering communities 14‐18 …”

Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

“…[1][2][3] As one of the important collective behaviors of complex networks, synchronization plays an important role in the study of complex networks. [4][5][6] Coupled neural networks (CNNs), as a special case of complex networks, its synchronization problem has been paid more attention and some achievements have been made. 4,[7][8][9] However, most of the current research results are based on single-layer networks, which often have limitations in practical applications and cannot reflect real-world characteristics well, so prompts researchers to investigate multilayer networks (MLNs).…”

Stability and Synchronization of Delayed Quaternion-Valued Neural Networks under Multi-Disturbances

“…Synchronization has attracted considerable research interest in recent years because it is an important dynamic behavior of coupled chaotic NNs. Similar to the equilibria stability of NNs, the delay phenomenon [6][7][8][15][16][17]19,20,28,29,[37][38][39][40][41][42][43][44][45][46][47][48], impulsive disturbances [6,14,27], and stochastic disturbances [38][39][40][43][44][45][46][48][49][50][51][52][53] should be considered when examining the synchronization control of chaotic NNs. Currently, most research results [37][38][39][40][41][42][43][44][45][46]…”