  Function plays an important role in mathematics and many science branches. As
the fast development of computer technology, more and more study on
computational function analysis, e.g., Fast Fourier Transform, Wavelet
Transform, Curve Function, are presented in these years. However, there are two
main problems in these approaches: 1) hard to handle the complex functions of
stationary and non-stationary, periodic and non-periodic, high order and low
order; 2) hard to generalize the fitting functions from training data to test
data. In this paper, a multiple regression based function fitting network that
solves the two main problems is introduced as a predicable function fitting
technique. This technique constructs the network includes three main parts: 1)
the stationary transform layer, 2) the feature encoding layers, and 3) the fine
tuning regression layer. The stationary transform layer recognizes the order of
input function data, and transforms non-stationary function to stationary
function. The feature encoding layers encode the raw input sequential data to a
novel linear regression feature that can capture both the structural and the
temporal characters of the sequential data. The fine tuning regression layer
then fits the features to the target ahead values. The fitting network with the
linear regression feature layers and a non-linear regression layer come up with
high quality fitting results and generalizable predictions. The experiments of
both mathematic function examples and the real word function examples verifies
the efficiency of the proposed technique.
