  Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based
model of artificial neural network for unsupervised learning. Recently, RBM is
well known to be a pre-training method of Deep Learning. In addition to visible
and hidden neurons, the structure of RBM has a number of parameters such as the
weights between neurons and the coefficients for them. Therefore, we may meet
some difficulties to determine an optimal network structure to analyze big
data. In order to evade the problem, we investigated the variance of parameters
to find an optimal structure during learning. For the reason, we should check
the variance of parameters to cause the fluctuation for energy function in RBM
model. In this paper, we propose the adaptive learning method of RBM that can
discover an optimal number of hidden neurons according to the training
situation by applying the neuron generation and annihilation algorithm. In this
method, a new hidden neuron is generated if the energy function is not still
converged and the variance of the parameters is large. Moreover, the
inactivated hidden neuron will be annihilated if the neuron does not affect the
learning situation. The experimental results for some benchmark data sets were
discussed in this paper.
