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

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 imp...

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Bibliographic Details
Published in:Neural processing letters 2021-06, Vol.53 (3), p.2029-2046
Main Authors: Shen, Zixiang, Li, Chuandong, Li, Yi
Format: Article
Language:English
Subjects:
Approximation
Artificial Intelligence
Complex Systems
Computational Intelligence
Computer Science
Computer simulation
Domains
Equilibrium
Estimation
Liapunov functions
Neural networks
Neurosciences
Pattern recognition
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Summary: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.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-021-10498-7