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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.
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Published: 24 February 2021
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