Nonlinear　system　modeling　plays　an　important　role　in　practical　engineering,　and　deep　learning-based　deep　belief　network　(DBN)　is　now　popular　in　nonlinear　system　modeling　and　identification　because　of　the　strong　learning　ability.　However,　the　existing　weights　optimization　for　DBN　is　based　on　gradient,　which　always　leads　to　a　local　optimum　and　a　poor　training　result.　In　this　paper,　a　DBN　with　partial　least　square　regression　(PLSR-DBN)　is　proposed　for　nonlinear　system　modeling,　which　focuses　on　the　problem　of　weights　optimization　for　DBN　using　PLSR.　Firstly,　unsupervised　contrastive　divergence　(CD)　algorithm　is　used　in　weights　initialization.　Secondly,　initial　weights　derived　from　CD　algorithm　are　optimized　through　layer-by-layer　PLSR　modeling　from　top　layer　to　bottom　layer.　Instead　of　gradient　method,　PLSR-DBN　can　determine　the　optimal　weights　using　several　PLSR　models,　so　that　a　better　performance　of　PLSR-DBN　is　achieved.　Then,　the　analysis　of　convergence　is　theoretically　given　to　guarantee　the　effectiveness　of　the　proposed　PLSR-DBN　model.　Finally,　the　proposed　PLSR-DBN　is　tested　on　two　benchmark　nonlinear　systems　and　an　actual　wastewater　treatment　system　as　well　as　a　handwritten　digit　recognition　(nonlinear　mapping　and　modeling)　with　high-dimension　input　data.　The　experiment　results　show　that　the　proposed　PLSR-DBN　has　better　performances　of　time　and　accuracy　on　nonlinear　system　modeling　than　that　of　other　methods.　(C)　2017　Elsevier　Ltd.　All　rights　reserved.