Pipeline damage identification based on an optimized back-propagation neural network improved by whale optimization algorithm

With the advantages of high economy and large transportation capacity, pipeline transportation is commonly used in industrial production. Pipeline damage induced by various factors will result in changes of physical properties, further leading to changes of dynamic parameters such as natural frequen...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Jg. 53; H. 10; S. 12937 - 12954
Hauptverfasser:	Wu, Lei, Mei, Jiangtao, Zhao, Shuo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2023 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Artificial neural networks Back propagation Back propagation networks Computer Science Damage assessment Damage detection Identification methods Machines Manufacturing Mechanical Engineering Neural networks Optimization Optimization algorithms Physical properties Processes Resonant frequencies Swarm intelligence Transportation Vibration mode Back-propagation neural network Whale optimization algorithm Damage location and degree Pipeline damage identification
ISSN:	0924-669X, 1573-7497
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!