Exponential stability for stochastic Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays

Global exponential stability for interval general bidirectional associative memory (BAM) neural networks with proportional delays

“…In , although authors dealt with exponential stability for BAM neural networks with proportional delays, they do not investigate interval general BAM neural networks with proportional delays. All the results in can not applied to model to obtain the global exponential stability for model , which implies that the obtained results of this paper are completely new and complement the previous studies to some extent.…”

“…The proof of Theorem is complete. □Remark The main contribution of this paper lies in the following aspects: (i) a new method based on constructing the novel delay differential inequality (without constructing the Lyapunov functional) is proposed, which is different from the past work on this topic; (ii) the obtained results can be easily checked by simple algebraic computation and less conservative than the previous studies; and (iii) the obtained results play an important in establishing a Q ∘ S routing algorithm based on BAM neural networks with proportional delays.Remark In the past decades, there are a large number of results on the global exponential stability for BAM neural networks with constant delays, time‐varying delays or distributed delays ; all the results are not involved in proportional delays. In , although authors dealt with exponential stability for BAM neural networks with proportional delays, they do not investigate interval general BAM neural networks with proportional delays.…”

“…In the past decades, there are a large number of results on the global exponential stability for BAM neural networks with constant delays, time-varying delays or distributed delays [34][35][36][37][45][46][47][48][49][50]; all the results are not involved in proportional delays. In [51,53,54], although authors dealt with exponential stability for BAM neural networks with proportional delays, they do not investigate interval general BAM neural networks with proportional delays.…”

“…As usual, the time delays considered for BAM neural networks can be classified as constant delays [39][40][41][42], time-varying delays [43][44][45][46], and distributed delays [47][48][49][50]. Here, we would like to point out that the proportional delay, which is a special delay type, exists in some fields such as physics, biology systems, control theory, and Web quality of service routing decision.…”

Boundedness and robust analysis of dynamics of nonlinear diffusion high-order Markovian jump time-delay system

“…In 1983, Cohen-Grossberg neural networks (CGNNs) were originally proposed in [8]. Since then, the CGNNs have gained increasing research attention [9][10][11][12][13][14] due to their extensive applications, such as pattern recognition, image and signal processing, quadratic optimization, and artificial intelligence. Whether the above application is successful depends on a key prerequisite that the system has some stability.…”