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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Vydáno v:	Knowledge-based systems Ročník 197; s. 105863
Hlavní autoři:	Chen, Yong, Li, Feng, Wang, Jiaxu, Tang, Baoping, Zhou, Xueming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 07.06.2020 Elsevier Science Ltd
Témata:	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 přístup:	Získat plný text
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