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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 12/2023

17.07.2023 | Original Article

Robust stability of complex-valued fractional-order neural networks with uncertain parameters based on new integral inequalities

verfasst von: Yushan Wang, Cheng-De Zheng, Meiyan Lin

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 12/2023

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Abstract

This paper investigates the stability of complex-valued (CV) fractional-order neural networks with uncertain parameters and neutral-type delay. Firstly, two improved reciprocally convex inequalities (RCIs) and three less-conservative integral inequalities are generalized to the complex-valued domain. Secondly, the existence, uniqueness and delay-independent robust stability conditions of the addressed networks are proposed based on the CV homeomorphism theorem. Thirdly, by constructing a Lyapunov–Krasovskii functional, delay-dependent robust stability conditions of the considered networks are derived by utilizing the improved complex-valued RCIs and integral inequalities. Finally, two simulation examples are given to show the effectiveness and practicality of the presented method.
Vorheriger Artikel UniSAr: a unified structure-aware autoregressive language model for text-to-SQL semantic parsing
Nächster Artikel General fine-grained event detection based on fusion of multi-information representation and attention mechanism

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Metadaten
Titel
Robust stability of complex-valued fractional-order neural networks with uncertain parameters based on new integral inequalities
verfasst von
Yushan Wang
Cheng-De Zheng
Meiyan Lin
Publikationsdatum
17.07.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 12/2023
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01899-2

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