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中文摘要：
深度学习是机器学习中的新兴研究领域，能够很好地用于解决目标识别、语言理解等复杂问题．玻尔兹曼机作为深度学习的典型代表近年来受到了广泛研究．鉴于玻尔兹曼机的理论意义和实际应用价值，系统综述了玻尔兹曼机的研究进展，首先概述了玻尔兹曼机的相关概念，包括单层反馈网络的结构和拓扑结构分类，然后详细描述了玻尔兹曼机的学习过程和几种典型学习算法，接着对近几年玻尔兹曼机研究的新进展进行了阐述，最后提出了玻尔兹曼机中有待进一步研究解决的问题．
英文摘要：
Being a new research area of machine learning, deep learning is good at solving some complex problems. As a representative of deep learning, Boltzmann machine is being widely studied. In view of the theoretical significance and practical value of Boltzmann machine, the research and development on Boltzmann machine are reviewed systematically. Firstly, some concepts about Boltzmann machine are summarized, which include configuration of Boltzmann machine as a single layer feedback network and classification of Boltzmann machine according to the topological structure, including general Boltzmann machine, semi-restricted Boltzmann machine and restricted Boltzmann machine. Secondly, the learning procedure of Boltzmann machine is reviewed in detail. Thirdly, several typical algorithms of Boltzmann machine are introduced, such as Gibbs sampling, parallel tempering, variational approach, stochastic approximation procedure, and contrastive divergence. Fourthly, the learning procedure of deep Boltzmann machine is described. New research and development on aspects of algorithms, models and practical application of Boltzmann machine in recent years are expounded then. Finally, the problems to be solved are pointed out.
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