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Neural Computing and Applications

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18.11.2015 | Original Article

Exponential stability analysis of delayed memristor-based recurrent neural networks with impulse effects

verfasst von: Huamin Wang, Shukai Duan, Chuandong Li, Lidan Wang, Tingwen Huang

Erschienen in: Neural Computing and Applications | Ausgabe 4/2017

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Abstract

In this paper, a generalized memristor-based recurrent neural network model with variable delays and impulse effects is considered. By using an impulsive delayed differential inequality and Lyapunov function, the exponential stability of the impulsive delayed memristor-based recurrent neural networks is investigated. Several exponential and uniform stability criteria of this impulsive delayed system are derived, which promotes the study of memristor-based recurrent neural networks. Finally, the effectiveness of obtained results is illustrated by two numerical examples.

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Titel: Exponential stability analysis of delayed memristor-based recurrent neural networks with impulse effects
verfasst von: Huamin Wang
Shukai Duan
Chuandong Li
Lidan Wang
Tingwen Huang
Publikationsdatum: 18.11.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 4/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-2094-6

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