Quantum recurrent encoder–decoder neural network for performance trend prediction of rotating machinery

Traditional neural networks generally neglect the primary and secondary relationships of input information and process the information indiscriminately, which leads to their bad nonlinear approximation capacity and low generalization ability. As a result, traditional neural networks always show poor...

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Vydané v:	Knowledge-based systems Ročník 197; s. 105863
Hlavní autori:	Chen, Yong, Li, Feng, Wang, Jiaxu, Tang, Baoping, Zhou, Xueming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 07.06.2020 Elsevier Science Ltd
Predmet:	Acceleration Algorithms Approximation Artificial intelligence Attention Attention mechanism Coders Degradation Entropy Information Machinery Mathematical analysis Matrices Networks Neural networks Noise reduction Performance degradation Performance prediction Performance trend prediction Predictions Quantum neuron Quantum recurrent encoder–decoder neural network (QREDNN) Recurrent Roller bearings Rotating machinery Rotation Solution space Trends Attention mechanism Performance trend prediction Rotating machinery Quantum neuron Artificial intelligence Quantum recurrent encoder–decoder neural network (QREDNN)
ISSN:	0950-7051, 1872-7409
On-line prístup:	Získať plný text
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