Vibration signal prediction model for the miniature transducer using deep learning network

  • Yen-Ta Chiang
  • Yu-Ting TsaiEmail author
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)


This work presents a deep artificial neural network (DNN) using input/output measurements of the miniature transducer. The DNN consists of a convolution network with one-dimensional convolution layers, pooling layers and full connection layers with ReLU activation function. In the training process, the coefficients of each layer are adapted to minimize the loss function as the least-square function. In the experiment tests, the numerous type of signals used for training and validation tests. As a result, the proposed model can fit the output response of the miniature transducer in high accuracy.


deep learning network miniature transducer 


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The authors would like to thank the Ministry of Science and Technology of Taiwan for financial support under Contract Nos. MOST 107-2221-E-035-061-.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Master’s Degree Program of electro-acousticFeng Chia UniversityTaichungTaiwan

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