Identification　of　a　general　nonlinear　noisy　system　viewed　as　an　estimation　of　a　predictor　function　is　studied　in　this　article.　A　measurement　fusion　method　for　the　predictor　function　estimate　is　proposed.　In　the　proposed　scheme,　observed　data　are　first　fused　by　using　an　optimal　fusion　technique,　and　then　the　optimal　fused　data　are　incorporated　in　a　nonlinear　function　estimator　based　on　a　robust　least　squares　support　vector　machine　(LS-SVM).　A　cooperative　learning　algorithm　is　proposed　to　implement　the　proposed　measurement　fusion　method.　Compared　with　related　identification　methods,　the　proposed　method　can　minimize　both　the　approximation　error　and　the　noise　error.　The　performance　analysis　shows　that　the　proposed　optimal　measurement　fusion　function　estimate　has　a　smaller　mean　square　error　than　the　LS-SVM　function　estimate.　Moreover,　the　proposed　cooperative　learning　algorithm　can　converge　globally　to　the　optimal　measurement　fusion　function　estimate.　Finally,　the　proposed　measurement　fusion　method　is　applied　to　ARMA　signal　and　spatial　temporal　signal　modeling.　Experimental　results　show　that　the　proposed　measurement　fusion　method　can　provide　a　more　accurate　model.