In　this　paper,　a　self-organizing　cascade　neural　network　(SCNN)　with　random　weights　is　proposed　for　nonlinear　system　modeling.　This　SCNN　is　constructed　via　simultaneous　structure　and　parameter　learning　processes.　In　structure　learning,　the　units,　which　lead　to　the　maximal　error　reduction　of　the　network,　are　selected　from　the　candidates　and　added　to　the　existing　network　one　by　one.　A　stopping　criterion　based　on　the　training　and　validation　errors　is　introduced　to　select　the　optimal　network　size　to　match　with　a　given　application.　In　parameter　learning,　the　weights　connected　with　the　output　units　are　incrementally　updated　without　gradients　or　generalized　inverses,　while　the　other　weights　are　randomly　assigned　and　no　need　to　be　tuned.　Then,　the　convergence　of　SCNN　is　analyzed.　Finally,　the　proposed　SCNN　is　tested　on　two　benchmark　nonlinear　systems　and　an　actual　municipal　sewage　treatment　system.　The　experiment　results　show　that　the　proposed　SCNN　has　better　performance　on　nonlinear　system　modeling　than　other　similar　methods.　(C)　2016　Published　by　Elsevier　B.V.