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دسته بندی:
شبکه های نورونی - neuron-networks
سال انتشار:
2020
عنوان انگلیسی مقاله:
Quantum recurrent encoder–decoder neural network for performance trend prediction of rotating machinery
ترجمه فارسی عنوان مقاله:
شبکه عصبی رمزگذار- رمزگذار مکرر کوانتومی برای پیش بینی روند عملکرد ماشین های چرخشی
منبع:
Sciencedirect - Elsevier - Knowledge-Based Systems, 197 (2020) 105863. doi:10.1016/j.knosys.2020.105863
نویسنده:
Yong Chen a, Feng Li a,b,c,∗, Jiaxu Wangb,c, Baoping Tang b, Xueming Zhou d
چکیده انگلیسی:
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
prediction accuracy in the performance degradation trend prediction of rotating machinery (RM).
In view of this, a novel neural network called quantum recurrent encoder–decoder neural network
(QREDNN) is proposed in this paper. In QREDNN, the attention mechanism is used to simultaneously
reconstruct encoder and decoder of QREDNN, so that QREDNN can fully excavate and pay attention
to important information but suppress the interference of redundant information to obtain better
nonlinear approximation capacity. On the other hand, the quantum neuron is used to construct a
new quantum gated recurrent unit (QGRU) in which activation values and weights are represented
by quantum rotation matrices. The QGRU can traverse the solution space more finely and has a lot
of multiple attractors, so it can replace the traditional recurrent unit of the encoder and decoder
and enhance the generalization ability and response speed of QREDNN. Moreover, the Levenberg–
Marquardt (LM) algorithm is introduced to improve the update speeds of the rotation angles of
quantum rotation matrices and the attention parameters of QREDNN. Based on the superiorities of
QREDNN, a new performance trend prediction method for RM is proposed, in which the denoised
fuzzy entropy (DFE) of vibration acceleration signal of RM is input into QREDNN as the performance
degradation feature for predicting the performance degradation trend of RM. The examples of
predicting the performance trend of rolling bearings demonstrate the effectiveness of our proposed
method.
Keywords: Quantum recurrent encoder–decoder | neural network (QREDNN) | Artificial intelligence | Attention mechanism | Quantum neuron | Performance trend prediction | Rotating machinery
قیمت: رایگان
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