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

A Non-Linear Magnetic Field Calibration Method for Filter-Based Magnetographs by Multilayer Perceptron