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Zabalza, J., Ren, J., Zheng, J., et al. (2016) Novel Segmented Stacked Autoencoder for Effective Dimensionality Reduction and Feature Extraction in Hyperspectral Imaging. Neurocomputing, 185, 1-10.
https://doi.org/10.1016/j.neucom.2015.11.044

has been cited by the following article:

• TITLE: Application of Improved Deep Auto-Encoder Network in Rolling Bearing Fault Diagnosis

  AUTHORS: Jian Di, Leilei Wang

  KEYWORDS: Fault Diagnosis, Rolling Bearing, Deep Auto-Encoder Network, CAPSO Algorithm, Feature Extraction

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.7, July 30, 2018

  ABSTRACT: Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.