中文摘要：

针对深度信念网（Deep belief network，DBN）预训练耗时长的问题，提出了一种基于自适应学习率的DBN（Adap-tive learning rate DBN，ALRDBN）．ALRDBN将自适应学习率引入到对比差度（Contrastive divergence，CD）算法中，通过自动调整学习步长来提高CD算法的收敛速度．然后设计基于自适应学习率的权值训练方法，通过网络性能分析给出学习率变化系数的范围．最后，通过一系列的实验对所设计的ALRDBN进行测试，仿真实验结果表明，ALRDBN的收敛速度得到了提高且预测精度也有所改善．

英文摘要：

A deep belief network with adaptive learning rate （ALRDBN） is proposed to solve the time-consuming problem in the pre-training period of DBN. The ALRDBN introduces the idea of adaptive learning rate into contrastive divergence （CD） algorithm and accelerates its convergence by a self-adjusting learning rate. The training method of weights in this case is designed, in which the adjusting scope of the coefficient in learning rate is determined by performance analysis. Finally, a series of experiments are carried out to test the performance of ALRDBN, and the corresponding results show that the convergence rate is accelerated significantly and the accuracy of prediction is improved as well.