Abstract：In recent years, the theory of deep learning is booming again. It is widely used in machine learning, visual recognition and
auditory recognition. Boltzmann machine is a typical deep learning neural network. There are many training algorithms for its net-
work weights, such as contrast dispersion (CD) algorithm, which is classical. However, the current algorithm cannot accurately ob-
tain the expected value of network thermal equilibrium state. Only approximate gradient values can be calculated. At the same time,
the algorithm has a large amount of computation and a long running time. In this paper, a method of RBM weight calculation is pro-
posed. Firstly, RBM is equivalent to Hopfield network. Then the weight matrix is designed by DHNN weight design method. Final-
ly, the RBM weight solving problem is transformed into the eigenvalue and eigenvector problem of DHNN weight matrix. An exam-
ple is given to illustrate the calculation process and the correctness of the algorithm is verified by the data.
张波. 基于D H N N 网络的玻尔兹曼机权值计算研究[J]. 电脑与电信, .
ZHANG Bo. Research on Weight Calculation of Boltzmann Machine Based on Hopfield Network. Computer & Telecommunication, 2020, 1(12): 53-57.
