The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network
Abstract
Deep belief network (DBN) is one of the most representative deep learning models. However, it has a disadvantage that the network structure and parameters are basically determined by experiences. In this article, an improved quantum-inspired differential evolution (MSIQDE), namely MSIQDE algorithm based on making use of the merits of the Mexh wavelet function, standard normal distribution, adaptive quantum state update, and quantum nongate mutation, is proposed to avoid premature convergence and improve the global search ability. Then, the MSIQDE with global optimization ability is used to optimize the parameters of the DBN to construct an optimal DBN model, which is further applied to propose a new fault classification, namely MSIQDE-DBN method. Finally, the vibration data of rolling bearings from the Case Western Reserve University and a real-world engineering application are carried out to verify the performance of the MSIQDE-DBN method. The experimental results show that the MSIQDE takes on better optimization performance, and the MSIQDE-DBN can obtain higher classification accuracy than the other comparison methods.
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Cites
A new convolutional neural network-based data-driven fault diagnosis method
Wen, L., Li, X., Gao, L., & Zhang, Y. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2774777 · Cited by 2,222 · cited 1×
“Therefore, the DBN is widely applied in fault classification [9]–[14].”
Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.025 · Cited by 1,710
Chen, Z., & Li, W. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2669947 · Cited by 883
An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem
Deng, W., Xu, J., & Zhao, H. (2019) · IEEE Access · doi:10.1109/access.2019.2897580 · Cited by 560
Self-citationTime series forecasting using a deep belief network with restricted Boltzmann machines
Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.03.047 · Cited by 527
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network
Shao, H., Jiang, H., Zhang, H., & Liang, T. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2745473 · Cited by 488
Digitized adiabatic quantum computing with a superconducting circuit
Barends, R., Shabani, A., Lamata, L., Kelly, J., Mezzacapo, A., Heras, U. L., Babbush, R., Fowler, A. G., Campbell, B., Chen, Y., et al. (2016) · Nature · doi:10.1038/nature17658 · Cited by 463
Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning
Sun, J., Yan, C., & Wen, J. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2759418 · Cited by 422
Enhanced Moth-flame optimizer with mutation strategy for global optimization
Xu, Y., Chen, H., Luo, J., Zhang, Q., Jiao, S., & Zhang, X. (2019) · Information Sciences · doi:10.1016/j.ins.2019.04.022 · Cited by 414
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Metadata
- Authors
- Deng, W., Liu, H., Xu, J., Zhao, H., & Song, Y.
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2020-10
- Volume
- 69
- Issue
- 10
- Pages
- 7319-7327
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W3013482090
- Cited by
- 429
- References
- 29
- FWCI
- 33.15
- Type
- journal article
- Language
- en
- Open access
- closed
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