2018
DOI: 10.1515/anly-2018-0011
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On the oscillation of impulsive vector partial differential equations with distributed deviating arguments

Abstract: In this paper, we consider a class of nonlinear impulsive neutral partial functional differential equations with continuous distributed deviating arguments. For this class, we establish sufficient conditions for the H-oscillation of the solutions, using impulsive differential inequalities and an averaging technique with two different boundary conditions. We provide an example to illustrate the main result.

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Cited by 4 publications
(4 citation statements)
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“…On the other hand, it is easy to see that u H (x, t) satisfies the boundary condition(9). This is a contradiction to the hypothesis.Similarly, if u H (x, t) is eventually negative, using Lemma 2.1, we easily obtain that u H (x, t) satisfies the scalar impulsive partial differential inequality(4). It is obvious that u H (x, t) satisfies the boundary condition(9).…”
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confidence: 84%
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“…On the other hand, it is easy to see that u H (x, t) satisfies the boundary condition(9). This is a contradiction to the hypothesis.Similarly, if u H (x, t) is eventually negative, using Lemma 2.1, we easily obtain that u H (x, t) satisfies the scalar impulsive partial differential inequality(4). It is obvious that u H (x, t) satisfies the boundary condition(9).…”
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confidence: 84%
“…Several authors worked on the oscillatory behaviour of impulsive partial differential equations with delays [11,14,21,23,28]. For the essential background on the oscillation theory of differential equations, we refer the reader to the monographs [17,32,33] and the references cited therein [3,5,9,22].…”
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confidence: 99%
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“…Its consequences were included in the book [15]. Chatzarakis et al [3], Kalaimani et al [13], Prakash et al [20], Sadhasivam et al [23,25] and Yang et al [30] studied the impulsive partial differential equations. The study of impulsive partial differential equations is motivated by various applications in population models [2], single species growth [6], feedback control prey-predator model [12], and various scientific models [29,31].…”
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confidence: 99%