2007
DOI: 10.1016/j.neucom.2006.09.007
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Passivity analysis of integro-differential neural networks with time-varying delays

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Cited by 69 publications
(36 citation statements)
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“…, 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%
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“…, 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%
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“…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.…”
Section: Introductionmentioning
confidence: 99%