New Passivity Criteria for Fuzzy Bam Neural Networks with Markovian Jumping Parameters and 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.…”

“…On the other hand, time delay is inevitable because of the finite switching speed of information processing and the inherent communication time of neurons; moreover, its existence may make the networks become unstable [26]. Thus, many interesting stability results on BAM neural networks with delays have been obtained [27][28][29][30][31][32][33][34][35][36][37]. In many cases, the stability of neural networks may be destroyed by inevitable system errors, external disturbance, and parameter fluctuation.…”

Improved Delay-dependent Robust Stability Analysis for Neutral-type Uncertain Neural Networks with Markovian jumping Parameters and Time-varying Delays

“…Recently, there were lots of research works on the dynamics analysis for delayed neural networks with Markovian jumping parameters have been reported in the literature [22][23][24][25][26][27][28][29][30][31][32][33][34]. For example, for neural networks with Markovian jumping parameters and time delays, the problem of stability analysis and passivity analysis have been investigated in [22][23][24] and [27,28], respectively. And the same problems have been proposed in [29][30][31][32] and [33,34] for the neutral-type delayed neural networks with Markovian jumping parameters.…”

Nonfragile passivity and passification of nonlinear singular networked control systems with randomly occurring controller gain fluctuation

“…The key idea of passivity theory is that a passive system can keep itself internally stable [20][21][22][23][24][25][26]. As a result, the passivity and passification problems have been a hot research area in the past years and a lot of applications based on passivity theory can be found in many areas, such as signal processing, stability analyzing, chaos control, and so on [27][28][29][30][31][32].…”