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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2021

21.09.2020 | Original Article

Efficiency in uncertain variational control problems

verfasst von: Savin Treanţă

Erschienen in: Neural Computing and Applications | Ausgabe 11/2021

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Abstract

In this paper, considering the applications of interval analysis in various fields (such as artificial intelligence, neural computation, genetic algorithms, information theory or fuzzy logic), a new class of interval-valued variational control problems governed by multiple integral functionals, first-order PDE and inequality constraints is studied. More precisely, efficiency conditions for the considered uncertain variational control problem are formulated and proved. The sufficiency of Karush–Kuhn–Tucker conditions is established under some invexity and \((\rho, b)\)-quasiinvexity assumptions of the involved functionals. In addition, the paper is completed with illustrative applications (describing the controlled behavior of an artificial neural system) and the corresponding algorithm.
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Metadaten
Titel
Efficiency in uncertain variational control problems
verfasst von
Savin Treanţă
Publikationsdatum
21.09.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05353-0

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