“…, k n }. Adding the left side of (16)(17)(18)(19), (24), (25) toV (t), and using (13)(14)(15), (20)(21)(22) and (23), then we havė (26) where (ḣ(t)) is defined in (7), ,M,N ,Ū ,V are defined in Theorem 1, and η…”
Section: Resultsmentioning
confidence: 99%
“…By using the assumption |ḣ(t)| ≤ µ and Lemma 2, it is known that there exist α 1 (t) ≥ 0 and α 2 (t) ≥ 0 satisfying α 1 (t) + α 2 (t) = 1, such thatḣ(t) = −α 1 (t)µ + α 2 (t)µ. Thus, (ḣ(t)) in (26) can be viewed as the convex combination of the matrices 1 and 2 , that is…”
Section: Resultsmentioning
confidence: 99%
“…The passivity framework is a promising approach to the stability analysis of delayed neural networks, because it can lead to general conclusions on stability. Recently, passivity analysis problem was widely investigated for continuous-time and discrete-time systems or neural networks with time-varying delays [23][24][25][26][27][28][29][30]. It is known that the passivity conditions proposed in [25,26] for continuous-time neural networks are delay-independent, which are recognized to be conservative, especially when the time delays are small.…”
mentioning
confidence: 99%
“…Recently, passivity analysis problem was widely investigated for continuous-time and discrete-time systems or neural networks with time-varying delays [23][24][25][26][27][28][29][30]. It is known that the passivity conditions proposed in [25,26] for continuous-time neural networks are delay-independent, which are recognized to be conservative, especially when the time delays are small. The delay-dependent passivity conditions were proposed in [28][29][30] for uncertain continuous-time neural networks with time-varying delay.…”
In this paper, the passivity analysis problem is investigated for uncertain neural networks with time-varying discrete and distributed delays. Based on direct delay decomposition idea and free-weighting matrix approach, several new delay-dependent passive criterions are derived in terms of linear matrix inequalities (LMIs), which can be easily checked by the Matlab LMI toolbox. Numerical examples show that the obtained results improve some existing ones.
“…, k n }. Adding the left side of (16)(17)(18)(19), (24), (25) toV (t), and using (13)(14)(15), (20)(21)(22) and (23), then we havė (26) where (ḣ(t)) is defined in (7), ,M,N ,Ū ,V are defined in Theorem 1, and η…”
Section: Resultsmentioning
confidence: 99%
“…By using the assumption |ḣ(t)| ≤ µ and Lemma 2, it is known that there exist α 1 (t) ≥ 0 and α 2 (t) ≥ 0 satisfying α 1 (t) + α 2 (t) = 1, such thatḣ(t) = −α 1 (t)µ + α 2 (t)µ. Thus, (ḣ(t)) in (26) can be viewed as the convex combination of the matrices 1 and 2 , that is…”
Section: Resultsmentioning
confidence: 99%
“…The passivity framework is a promising approach to the stability analysis of delayed neural networks, because it can lead to general conclusions on stability. Recently, passivity analysis problem was widely investigated for continuous-time and discrete-time systems or neural networks with time-varying delays [23][24][25][26][27][28][29][30]. It is known that the passivity conditions proposed in [25,26] for continuous-time neural networks are delay-independent, which are recognized to be conservative, especially when the time delays are small.…”
mentioning
confidence: 99%
“…Recently, passivity analysis problem was widely investigated for continuous-time and discrete-time systems or neural networks with time-varying delays [23][24][25][26][27][28][29][30]. It is known that the passivity conditions proposed in [25,26] for continuous-time neural networks are delay-independent, which are recognized to be conservative, especially when the time delays are small. The delay-dependent passivity conditions were proposed in [28][29][30] for uncertain continuous-time neural networks with time-varying delay.…”
In this paper, the passivity analysis problem is investigated for uncertain neural networks with time-varying discrete and distributed delays. Based on direct delay decomposition idea and free-weighting matrix approach, several new delay-dependent passive criterions are derived in terms of linear matrix inequalities (LMIs), which can be easily checked by the Matlab LMI toolbox. Numerical examples show that the obtained results improve some existing ones.
“…Therefore, passivity theory has received lots of attention. In recent years, many researchers have studied the passivity of fuzzy systems [35][36][37][38] and neural networks [39][40][41][42][43][44]. In [30], Zhao and Hill presented a concept of passivity for switched systems using multiple storage functions.…”
We investigate input passivity and output passivity for a generalized complex network with non-linear, time-varying, non-symmetric and delayed coupling. By constructing some suitable Lyapunov functionals, several sufficient conditions ensuring input passivity and output passivity are derived for complex dynamical networks. Finally, two numerical examples are given to show the effectiveness of the obtained results.
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