Adaptive marginalized stacked denoising autoencoders and its application

In this paper, we propose a modified marginalized autoencoders. Here, the noise adding way at a fixed rate in marginalized autoencoders is replaced by the adaptive noise injection. Compared with the traditional marginalized autoencoders, the proposed method obviously enlarges the recognition perform...

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Veröffentlicht in:Chinese Control Conference S. 4107 - 4112
Hauptverfasser: Yu, Zhi-bin, Chen, Chun-xia, Pang, Rong, Chen, Tao-wei
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: TCCT 01.07.2016
Schlagworte:
adaptive noise
Conferences
High speed trains
high-speed train
Manifolds
Neurons
Noise
Noise level
Noise reduction
Recognition
stacked autoencoders
Support vector machines
Training
Vibration
Vibrations
Wear
wheel wear state monitoring
Wheels
ISSN:1934-1768
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Zusammenfassung:In this paper, we propose a modified marginalized autoencoders. Here, the noise adding way at a fixed rate in marginalized autoencoders is replaced by the adaptive noise injection. Compared with the traditional marginalized autoencoders, the proposed method obviously enlarges the recognition performance. Furthermore, the proposed method is applied to identify high-speed train wheel wear conditions. Features of high speed train wheels wear vibration signals are abstracted by using the adaptive noise marginalized autoencoders, and the features is used to realize the wheel wear characteristics of vibration signal recognitions as the input of support vector machine (SVM). The experimental results show that the accuracy of the new method for identifying high-speed train wheel wear conditions is 99.8% on average.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:1934-1768
DOI:10.1109/ChiCC.2016.7553994