The　model　bias　caused　by　input　outliers　is　a　dramatic　obstacle　to　the　application　of　models　in　industrial　processes.　To　cope　with　this　problem,　this　article　proposes　a　robust　modeling　method　based　on　frequency　reconstructed　fuzzy　neural　network　(FRFNN)　for　industrial　process.　The　robust　modeling　consists　of　two　parts:　One　is　feature　extraction,　where　a　Fourier-based　filter　is　developed　with　input　data　denoising.　It　enables　the　model　to　suppress　high-frequency　input　noises　and　burst　outliers.　The　other　one　is　feature　representation　that　is　realized　with　a　FRFNN.　The　soft　margins　of　membership　functions　of　FRFNN　are　designed　with　Fourier　estimation　of　outliers,　which　have　the　capability　of　outlier-tolerant　for　filtered-residual　outliers.　Moreover,　an　adaptive　gradient　descent　algorithm　is　introduced　to　update　the　model　parameters.　Based　on　the　adaptive　learning　rate　decaying　with　outliers,　this　algorithm　is　insensitive　to　the　bias　effect　of　outliers　and　also　maintains　convergence.　Finally,　the　proposed　robust　modeling　method　is　tested　on　two　real-world　industrial　datasets　with　input　outliers.　The　experimental　results　demonstrate　that　the　proposed　robust　modeling　method　can　strengthen　robustness　and　achieve　superior　performance　over　other　previous　methods.