Name
Title | ||
|---|---|---|
Quantum weighted long short-term memory neural network and its application in state degradation trend prediction of rotating machinery. |
Abstract | ||
|---|---|---|
Classical long short-term memory neural network (LSTMNN) generally faces the challenges of poor generalization property and low training efficiency in state degradation trend prediction of rotating machinery. In this paper, a novel quantum neural network called quantum weighted long short-term memory neural network (QWLSTMNN) is proposed. First, quantum bits are introduced into the long short-term memory unit to express network weights and activity values. Then, a new learning algorithm based on quantum phase-shift gate and quantum gradient descent is presented to quickly update the quantum parameters of weight qubits and activity qubits. The above characteristics endow QWLSTMNN with better nonlinear approximation capability, higher generalization property and faster convergence speed than LSTMNN. State degradation trend prediction for rolling bearings demonstrates that higher prediction accuracy and higher computational efficiency can be obtained due to the advantages of QWLSTMNN in terms of nonlinear approximation capability, generalization property and convergence speed. It is believed that the proposed method based on QWLSTMNN is effective for state degradation trend prediction of rotating machinery. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neunet.2018.07.004 | Neural Networks |
Keywords | Field | DocType |
Quantum weighted long short-term memory neural network (QWLSTMNN),Quantum computation,Wavelet packet energy entropy error,Trend prediction,Rotating machinery | Convergence (routing),Quantum,Gradient descent,Mathematical optimization,Quantum neural network,Algorithm,Degradation (geology),Bearing (mechanical),Artificial neural network,Qubit,Mathematics | Journal |
Volume | Issue | ISSN |
106 | 1 | 0893-6080 |
Citations | PageRank | References |
1 | 0.37 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Li | 1 | 1 | 0.37 |
| Wang Xiang | 2 | 1 | 0.71 |
| Jiaxu Wang | 3 | 5 | 1.16 |
| Xueming Zhou | 4 | 1 | 0.37 |
| Baoping Tang | 5 | 1 | 1.72 |