An improved arithmetic optimization algorithm for training feedforward neural networks under dynamic environments

This paper proposes an improved Arithmetic Optimization Algorithm (AOA) to train artificial neural networks (ANNs) under dynamic environments. Despite many successful applications of metaheuristic training of ANNs, these studies assume static environments, which might not be realistic in real-world...

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Vydané v:	Knowledge-based systems Ročník 263; s. 110274
Hlavní autori:	Gölcük, İlker, Ozsoydan, Fehmi Burcin, Durmaz, Esra Duygu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 05.03.2023
Predmet:	Arithmetic optimization algorithm Artificial neural networks Concept drift Dynamic optimization Arithmetic optimization algorithm Concept drift Artificial neural networks Dynamic optimization
ISSN:	0950-7051, 1872-7409
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