Institute for the Conservation of Cultural Heritage, Shanghai University, Shanghai 200444, China.
School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
Sensors (Basel). 2021 Feb 12;21(4):1318. doi: 10.3390/s21041318.
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.
本文提出了一种基于深度置信网络的古陶瓷多光谱数据无损鉴别方法。利用一阶微分和二阶微分预处理光谱数据之间的差异，提出了分数阶微分算法来增强光谱细节。在预训练过程中，采用无监督多层限制玻尔兹曼机（RBM）提取一些高层特征。RBM 训练的一些权重和偏置值用于初始化反向传播（BP）神经网络。RBM 深度置信网络通过 BP 神经网络进行微调，以提高网络训练的主动性，从而克服由于权重参数随机初始化而导致的网络局部最优限制。提出了随机失活策略到 RBM 网络中，以解决小样本光谱数据的过拟合问题。实验结果表明，与其他一些方法相比，该方法对陶瓷的识别性能优异。
