  Determining the universality class of a system exhibiting critical phenomena
is one of the central problems in physics. There are several methods to
determine this universality class from data. As methods performing collapse
plots onto scaling functions, polynomial regression, which is less accurate,
and Gaussian process regression, which provides high accuracy and flexibility
but is computationally heavy, have been proposed. In this paper, we propose a
regression method using a neural network. The computational complexity is only
linear in the number of data points. We demonstrate the proposed method for the
finite-size scaling analysis of critical phenomena on the two-dimensional Ising
model and bond percolation problem to confirm the performance. This method
efficiently obtains the critical values with accuracy in both cases.
