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中文摘要：
针对受限玻尔兹曼机（restricted Boltzmann machines,RBM）算法对时序数据预测存在抽取抽象特征向量能力较差和梯度下降能力有限的问题,基于CRBM（conditional restricted Boltzmann machines）算法以及信念网络（deep belief network,DBN）模型,构建了一种非线性的CRBM-DBN深度学习模型,并采用高斯分布处理输入特征值和对比散度抽样,用于预测时序数据.实验以浙江省近岸海域赤潮时序数据作为输入特征值,讨论该模型的深度及参数选取,并与经典的深度学习模型RBM、DAE和浅层学习中的BP神经网络进行对比,实验验证CRBM对于赤潮时序数据的预测拟合度要明显优于其他3种模型,该模型可有效用于赤潮类时序数据的趋势性预测.
英文摘要：
Restricted Boltzmann machines（RBM）algorithm has a poor performance in extracting feature vector and gradient descent when it is used to predict time-series data.To solve the above problems,a non-linear deep learning model was constructed based on conditional restricted Boltzmann machines（CRBM）combining with deep belief network（DBN）.The model processed the input feature vectors with Gaussian distribution and samples with classical contrastive divergence to predict continuous time-series data.Our experiment adopted the time-series data of red tide in Zhejiang costal,and discussed the selection of network depth and training parameters in the model,then compared the deep learning model to classical RBM,DAE deep learning network and BP neural network shadow learning.The results showed that the prediction fitting of CRBM was superior to the other three models.This model can effectively predict the time-series of red tide.
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