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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2021

26.03.2021

Estimation of the Domain of Attraction of Discrete-Time Impulsive Cohen-Grossberg Neural Networks Model With Impulse Input Saturation

verfasst von: Zixiang Shen, Chuandong Li, Yi Li

Erschienen in: Neural Processing Letters | Ausgabe 3/2021

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Abstract

This paper aims at estimating the domain of attraction of discrete-time impulsive neural networks with impulse input saturation by using Lyapunov function. When an equilibrium point is locally asymptotically stable, we estimate the size of its domain of attraction and then analyze the effects of impulse input saturation. Two numerical examples are presented to unfold the effectiveness of the theoretical results.
Vorheriger Artikel Attention-Based Deep Gated Fully Convolutional End-to-End Architectures for Time Series Classification
Nächster Artikel Weighted Discriminative Sparse Representation for Image Classification

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Metadaten
Titel
Estimation of the Domain of Attraction of Discrete-Time Impulsive Cohen-Grossberg Neural Networks Model With Impulse Input Saturation
verfasst von
Zixiang Shen
Chuandong Li
Yi Li
Publikationsdatum
26.03.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10498-7

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