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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 14/2020

02.11.2019 | Original Article

Global dissipativity of high-order Hopfield bidirectional associative memory neural networks with mixed delays

verfasst von: Chaouki Aouiti, Rathinasamy Sakthivel, Farid Touati

Erschienen in: Neural Computing and Applications | Ausgabe 14/2020

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Abstract

In this paper, the problem of the global dissipativity of high-order Hopfield bidirectional associative memory neural networks with time-varying coefficients and distributed delays is discussed. By using Lyapunov–Krasovskii functional method, inequality techniques and linear matrix inequalities, a novel set of sufficient conditions for global dissipativity and global exponential dissipativity for the addressed system is developed. Further, the estimations of the positive invariant set, globally attractive set and globally exponentially attractive set are found. Finally, two examples with numerical simulations are provided to support the feasibility of the theoretical findings.
Vorheriger Artikel An improved meta-heuristic method to maximize the penetration of distributed generation in radial distribution networks
Nächster Artikel Modified Zhang and Xu’s distance measure for Pythagorean fuzzy sets and its application to pattern recognition problems

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Metadaten
Titel
Global dissipativity of high-order Hopfield bidirectional associative memory neural networks with mixed delays
verfasst von
Chaouki Aouiti
Rathinasamy Sakthivel
Farid Touati
Publikationsdatum
02.11.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 14/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04552-8

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