Aiming　at　the　problem　that　the　membrane　bio-reactor(MBR)　wastewater　treatment　process　is　susceptible　to　the　influence　of　external　disturbances　such　as　influent　water　quantity　and　water　quality　fluctuations,　which　makes　it　difficult　to　accurately　measure　the　membrane　permeability,　a　soft-sensor　method　for　permeability　of　membrane　based　on　an　adaptive　robust　fuzzy　neural　network(ARFNN)　is　proposed.　Firstly,　a　symmetric　anti-noise　loss　function　is　constructed　to　reduce　the　sensitivity　of　the　model　to　external　interference　and　improve　the　robustness　of　the　soft　measurement　model.　Then,　an　adaptive　gradient　descent　algorithm　is　designed　to　dynamically　optimize　the　model　parameters　and　improve　the　detection　accuracy　of　the　soft　measurement　model.　Finally,　the　convergence　of　the　ARFNN　is　verified　using　the　Lyapunov　function　to　analyze　the　model＇s　robustness,　which　ensures　the　model＇s　convergence　speed　and　anti-interference　ability.　The　designed　ARFNN-based　membrane　permeability　soft-sensor　method　is　applied　to　the　actual　wastewater　treatment　process,　and　the　experimental　results　show　that　the　model　can　not　only　realize　the　online　detection　of　membrane　permeability,　but　also　obtain　high　detection　accuracy　under　the　condition　of　external　interference.　©　2025　Northeast　University.　All　rights　reserved.