A Pythagorean-Type Fuzzy Deep Denoising Autoencoder for Industrial Accident Early Warning

Early warning is crucial for preventing industrial accidents and mitigating damage, but current methods are often time-consuming, error-prone, and incompetent to deal with uncertainty. This paper presents a fuzzy deep neural network for early warning of industrial accidents, which equips the classic...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 25; H. 6; S. 1561 - 1575
Hauptverfasser:	Zheng, Yu-Jun, Chen, Sheng-Yong, Xue, Yu, Xue, Jin-Yun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accident early warning Accidents Algorithms Aluminum Artificial neural networks biogeography-based optimization (BBO) Damage prevention deep denoising autoencoder (DDAE) deep learning evolutionary learning Explosions Fuzzy logic Fuzzy sets Heuristic methods Industrial accidents Machine learning Mathematical models Neural networks Noise reduction Optimization Order parameters pythagorean fuzzy set (PFS) Robustness (mathematics) Uncertainty
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!