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Erschienen in: Neural Processing Letters 1/2019

14.03.2018

New Results on Convergence of CNNs with Neutral Type Proportional Delays and D Operator

verfasst von: Guangyi Yang, Weipin Wang

Erschienen in: Neural Processing Letters | Ausgabe 1/2019

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Abstract

Based on differential inequality technique, we show that all solutions of a class of cellular neural networks with neutral type proportional delays and D operator converge exponentially to zero vector. In particular, we obtain the convergence rate estimation for global exponential stability of the addressed system. We also give two simulations examples to verify our theoretical findings.

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Literatur
1.
2.
Zurück zum Zitat Wu J (2001) Introduction to neural dynamics and signal trasmission delay. Walter de Gruyter, BerlinCrossRef Wu J (2001) Introduction to neural dynamics and signal trasmission delay. Walter de Gruyter, BerlinCrossRef
3.
Zurück zum Zitat Kwon O, Lee S, Park J (2010) Improved results on stability analysis of neuralnet works with time-varying delays: novel delay-dependent criteria. Mod Phys Lett B 24:775–789CrossRefMATH Kwon O, Lee S, Park J (2010) Improved results on stability analysis of neuralnet works with time-varying delays: novel delay-dependent criteria. Mod Phys Lett B 24:775–789CrossRefMATH
4.
Zurück zum Zitat Kwon O, Park J (2009) Exponential stability analysis for uncertain neural networks with interval time-varying delays. Appl Math Comput 212:530–541MathSciNetMATH Kwon O, Park J (2009) Exponential stability analysis for uncertain neural networks with interval time-varying delays. Appl Math Comput 212:530–541MathSciNetMATH
5.
Zurück zum Zitat Arik S, Orman Z (2005) Global stability analysis of Cohen–Grossberg neural networks with time-varying delays. Phys Lett A 341:410–421CrossRefMATH Arik S, Orman Z (2005) Global stability analysis of Cohen–Grossberg neural networks with time-varying delays. Phys Lett A 341:410–421CrossRefMATH
6.
Zurück zum Zitat Kwon OM, Park JH, Lee SM, Cha EJ (2013) Analysis on delay-dependent stability for neural networks with time-varying delays. Neurocomputing 103:114–120CrossRef Kwon OM, Park JH, Lee SM, Cha EJ (2013) Analysis on delay-dependent stability for neural networks with time-varying delays. Neurocomputing 103:114–120CrossRef
7.
Zurück zum Zitat Fang M, Park JH (2013) Non-fragile synchronization of neural networks with time-varying delay and randomly occurring controller gain fluctuation. Appl Math Comput 219:8009–8017MathSciNetMATH Fang M, Park JH (2013) Non-fragile synchronization of neural networks with time-varying delay and randomly occurring controller gain fluctuation. Appl Math Comput 219:8009–8017MathSciNetMATH
8.
Zurück zum Zitat Rakkiyappan R, Balasubramaniam P (2008) Delay-dependent asymptotic stability for stochastic delayed recurrent neural networks with time varying delays. Appl Math Comput 198:526–533MathSciNetMATH Rakkiyappan R, Balasubramaniam P (2008) Delay-dependent asymptotic stability for stochastic delayed recurrent neural networks with time varying delays. Appl Math Comput 198:526–533MathSciNetMATH
9.
Zurück zum Zitat Mandal S, Majee NC (2011) Existence of periodic solutions for a class of Cohen–Grossberg type neural networks with neutral delays. Neurocomputing 74(6):1000–1007CrossRef Mandal S, Majee NC (2011) Existence of periodic solutions for a class of Cohen–Grossberg type neural networks with neutral delays. Neurocomputing 74(6):1000–1007CrossRef
10.
Zurück zum Zitat Gui Z, Ge W, Yang X (2007) Periodic oscillation for a Hopfield neural networks with neutral delays. Phys Lett A 364(3–4):267–273CrossRefMATH Gui Z, Ge W, Yang X (2007) Periodic oscillation for a Hopfield neural networks with neutral delays. Phys Lett A 364(3–4):267–273CrossRefMATH
11.
Zurück zum Zitat Xiao B (2009) Existence and uniqueness of almost periodic solutions for a class of Hopfield neural networks with neutral delays. Appl Math Lett 22:528–533MathSciNetCrossRefMATH Xiao B (2009) Existence and uniqueness of almost periodic solutions for a class of Hopfield neural networks with neutral delays. Appl Math Lett 22:528–533MathSciNetCrossRefMATH
12.
Zurück zum Zitat Liu B (2015) Pseudo almost periodic solutions for neutral type CNNs with continuously distributed leakage delays. Neurocomputing 148:445–454CrossRef Liu B (2015) Pseudo almost periodic solutions for neutral type CNNs with continuously distributed leakage delays. Neurocomputing 148:445–454CrossRef
13.
Zurück zum Zitat Yao L (2018) Global convergence of CNNs with neutral type delays and \(D\) operator. Neural Comput Appl 29:105–109CrossRef Yao L (2018) Global convergence of CNNs with neutral type delays and \(D\) operator. Neural Comput Appl 29:105–109CrossRef
14.
Zurück zum Zitat Ockendon JR, Tayler AB (1971) The dynamics of a current collection system for an electric locomotive. Proc R Soc Lond Ser A Math Phys Eng Sci 322(1551):447–468CrossRef Ockendon JR, Tayler AB (1971) The dynamics of a current collection system for an electric locomotive. Proc R Soc Lond Ser A Math Phys Eng Sci 322(1551):447–468CrossRef
15.
Zurück zum Zitat Liu B (2016) Global exponential convergence of non-autonomous cellular neural networks with multi-proportional delays. Neurocomputing 191:352–355CrossRef Liu B (2016) Global exponential convergence of non-autonomous cellular neural networks with multi-proportional delays. Neurocomputing 191:352–355CrossRef
16.
Zurück zum Zitat Liu B (2017) Finite-time stability of CNNs with neutral proportional delays and time-varying leakage delays. Math Methods Appl Sci 40:167–174MathSciNetCrossRefMATH Liu B (2017) Finite-time stability of CNNs with neutral proportional delays and time-varying leakage delays. Math Methods Appl Sci 40:167–174MathSciNetCrossRefMATH
17.
Zurück zum Zitat Yu Y (2016) Global exponential convergence for a class of neutral functional differential equations with proportional delays. Math Methods Appl Sci 39:4520–4525MathSciNetCrossRefMATH Yu Y (2016) Global exponential convergence for a class of neutral functional differential equations with proportional delays. Math Methods Appl Sci 39:4520–4525MathSciNetCrossRefMATH
18.
Zurück zum Zitat Yu Y (2016) Global exponential convergence for a class of HCNNs with neutral time-proportional delays. Appl Math Comput 285:1–7MathSciNetMATH Yu Y (2016) Global exponential convergence for a class of HCNNs with neutral time-proportional delays. Appl Math Comput 285:1–7MathSciNetMATH
Metadaten
Titel
New Results on Convergence of CNNs with Neutral Type Proportional Delays and D Operator
verfasst von
Guangyi Yang
Weipin Wang
Publikationsdatum
14.03.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9818-4

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