  For filter-based magnetographs, the linear calibration method under the
weak-field assumption is usually adopted; this leads to magnetic saturation
effect in the regions with strong magnetic field. This article explores a new
method to overcome the above disadvantage using a multilayer perceptron
network, which we call MagMLP, based on a back-propagation algorithm with one
input layer, five hidden layers, and one output layer. We use the data from the
\textit{Spectropolarimeter} (SP) on board \textit{Hinode} to simulate
single-wavelength observations for the model training, and take into account
the influence of the Doppler velocity field and the filling factor. The
training results show that the linear fitting coefficient (LFC) of the
transverse field reaches above 0.91, and that of the longitudinal field is
above 0.98. The generalization of the models is good because the corresponding
LFCs are above 0.9 for the test subsets. Compared with the linear calibration
method, the MagMLP is much more effective on dealing with the magnetic
saturation effect. Analyzing an active region, the results of the linear
calibration present an evident magnetic saturation effect in the umbra regions;
the corresponding systematic error reaches values greater than 1000 G in most
areas, or even exceeds 2000 G at some pixels. However, the results of MagMLP at
these locations are very close to the inversion results, and the systematic
errors are basically within 300 G. In addition, we find that there are many
"bright spots" and "dark spots" on the inclination angle images from the
inversion results of \textit{Hinode}/SP with values of 180 and 0 degrees,
respectively, where the inversion is not reliable and does not produce a good
result; the MagMLP handles these points well.
