This　article　proposes　a　novel　randomized　neuro-fuzzy　model　called　fuzzy　stochastic　configuration　networks　(F-SCNs),　which　integrates　the　Takagi-Sugeno　(T-S)　fuzzy　inference　system　into　SCNs　to　enhance　its　fuzzy　inference　capability.　Unlike　original　SCNs,　the　hidden　layer　in　SCNs　is　replaced　by　the　T-S　fuzzy　inference　module,　which　is　responsible　for　fuzzifying　the　input　data　and　performing　fuzzy　reasoning.　The　fuzzy　rules　generated　by　the　fuzzy　module　are　directly　connected　to　the　output　layer　of　the　network.　In　addition,　an　enhancement　layer　is　added　between　the　fuzzy　module＇s　output　and　the　output　layer　of　the　network　to　extract　nonlinear　information　contained　in　fuzzy　rules.　The　parameters　of　fuzzy　systems　are　determined　by　the　distribution　characteristics　of　the　input-output　data　of　the　network,　which　enhances　the　interpretability　of　the　model.　Moreover,　the　parameters　of　the　neuro-fuzzy　model　are　learned　by　stochastic　configuration　algorithms.　Therefore,　the　model　inherits　the　fast　learning　speed　and　universal　approximation　capability　of　SCNs.　A　series　of　simulation　experiments　are　carried　out,　including　nonlinear　dynamic　system　identification,　sequence　prediction,　and　benchmark　data　modeling　from　the　real　world　to　verify　the　feasibility　and　effectiveness　of　the　proposed　method.　Finally,　a　soft　sensing　model　for　the　effluent　total　phosphorus　concentration　in　wastewater　treatment　processes　is　developed　based　on　the　proposed　F-SCNs.　The　results　show　that　the　proposed　method　has　good　potential　for　nonlinear　system　modeling　tasks　compared　to　some　classical　neuro-fuzzy　and　nonfuzzy　models.