Dropout Deep Belief Network Based Chinese Ancient Ceramic Non-Destructive Identification
Open Access
- 12 February 2021
- Vol. 21 (4), 1318
- https://doi.org/10.3390/s21041318
Abstract
A non-destructive identification method was developed here based on dropout deep belief network in multi-spectral data of ancient ceramic. A fractional differential algorithm was proposed to enhance the spectral details by making use of the difference between the first and second-order differential pre-process spectral data. An unsupervised multi-layer restricted Boltzmann machine (RBM) was employed to extract some high-level features during pre-training. Some weight and bias values trained by RBM were used to initialize a back propagation (BP) neural network. The RBM deep belief network was fine-tuned by the BP neural network to promote the initiative performance of network training, which helped to overcome local optimal limitation of the network due to the random initializing weight parameter. The dropout strategy has been put forward into the RBM network to solve the over-fitting of small sample spectral data. The experimental results show that the proposed method has excellent recognition performance of the ceramics by comparisons with some other ones.Keywords
Funding Information
- National Key R&D Program of China (2020YFC1523004, 2019YFC1520500)
This publication has 44 references indexed in Scilit:
- A multi-spectroscopic study for the characterization and definition of production techniques of German ceramic sherdsMicrochemical Journal, 2016
- Elemental Characterization by EDXRF of Imperial Longquan Celadon Porcelain Excavated from Fengdongyan Kiln, Dayao CountyArchaeometry, 2014
- Key wavelengths selection from near infrared spectra using Monte Carlo sampling–recursive partial least squaresChemometrics and Intelligent Laboratory Systems, 2013
- Prediction of biomass gross calorific values using visible and near infrared spectroscopyBiomass and Bioenergy, 2012
- Advanced technique for non-destructive testing of friction stir welding of metalsMeasurement, 2010
- Thermal analysis of Romanian ancient ceramicsJournal of Thermal Analysis and Calorimetry, 2009
- Outlier detection in near-infrared spectroscopic analysis by using Monte Carlo cross-validationScience in China Series B Chemistry, 2008
- Fractional Splines and WaveletsSIAM Review, 2000
- Fractional calculus and the evolution of fractal phenomenaPhysica A: Statistical Mechanics and its Applications, 1999
- The fractional Fourier transform and time-frequency representationsIEEE Transactions on Signal Processing, 1994