2015
DOI: 10.1016/j.amc.2015.06.030
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Improved stability analysis of uncertain neutral type neural networks with leakage delays and impulsive effects

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Cited by 37 publications
(7 citation statements)
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“…In real world, the transmission delays often appear in leakage terms, which are called leakage delays [10]. It is well known that leakage delays have been incorporated into neural networks by many researchers [11][12][13][14]. However, leakage delays of neural networks in most bibliographies listed above are constants.…”
Section: Mathematical Problems In Engineeringmentioning
confidence: 99%
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“…In real world, the transmission delays often appear in leakage terms, which are called leakage delays [10]. It is well known that leakage delays have been incorporated into neural networks by many researchers [11][12][13][14]. However, leakage delays of neural networks in most bibliographies listed above are constants.…”
Section: Mathematical Problems In Engineeringmentioning
confidence: 99%
“…In the next section, we introduce some definitions and state several lemmas which will be essential to our proofs. In Section 3, by constructing a suitable Lyapunov functional, some new criteria are obtained to ensure the exponential synchronization of systems (5) and (9) under the adaptive feedback controller (11) and (12). Numerical simulations are carried out in Section 4 to illustrate the feasibility of the main theoretical results.…”
Section: Mathematical Problems In Engineeringmentioning
confidence: 99%
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“…Moreover, in practical implementation of neural networks, uncertainties are unavoidable because of the existence of modeling errors and external disturbances, which also result in instability and poor performance. So it is essential to introduce such uncertainties into our system and the related issues that have been reported in the literature [21,22,23,24].…”
Section: Introductionmentioning
confidence: 99%
“…In this paper, we will study the neutral-type neural networks when the neutral term has the D− operator form which is shown by Ax(t). For more references about neutral-type neural networks with mixed delays, see [14,15].…”
Section: Introductionmentioning
confidence: 99%