To　improve　the　performance　of　nonlinear　system　modeling,　this　study　proposes　a　feature　clustering-based　adaptive　modular　neural　network　(FC-AMNN)　by　simulating　information　processing　mechanism　of　human　brains　in　the　way　that　different　information　is　processed　by　different　modules　in　parallel.　Firstly,　features　are　clustered　using　an　adaptive　feature　clustering　algorithm,　and　the　number　of　modules　in　FC-AMNN　is　determined　by　the　number　of　feature　clusters　automatically.　The　features　in　each　cluster　are　then　allocated　to　the　corresponding　module　in　FC-AMNN.　Then,　a　self-constructive　RBF　neural　network　based　on　Error　Correction　algorithm　is　adopted　as　the　subnetwork　to　study　the　allocated　features.　All　modules　work　in　parallel　and　are　finally　integrated　using　a　Bayesian　method　to　obtain　the　output.　To　demonstrate　the　effectiveness　of　the　proposed　model,　FC-AMNN　is　tested　on　several　UCI　benchmark　problems　as　well　as　a　practical　problem　in　wastewater　treatment　process.　The　experimental　results　show　that　the　FC-AMNN　can　achieve　a　better　generalization　performance　and　an　accurate　result　for　nonlinear　system　modeling　compared　with　other　modular　neural　networks.　(C)　2019　ISA.　Published　by　Elsevier　Ltd.　All　rights　reserved.