The　radial　basis　function　network-based　on　state-dependent　autoregressive　(RBF-AR)　model　is　widely　used　in　the　modeling　and　prediction　of　nonlinear　time　series.　The　parameters　estimation　of　RBF-AR　model　is　a　typical　separable　nonlinear　least　squares　problem.　Variable　Projection　(VP)　algorithm　is　an　efficient　algorithm　to　solve　this　problem.　However,　The　actual　solution　may　be　a　ill-conditioned　problems,　and　the　classical　VP　algorithm　may　have　a　over-fitting　phenomenon.　which　makes　the　estimated　linear　parameters　too　large,　resulting　in　poor　generalization　performance　of　the　model.　Aiming　at　this　kind　of　problem,　this　paper　uses　expectation　maximization　(EM)　algorithm　to　automatically　select　the　appropriate　regularization　parameters,　and　combines　with　the　high　efficiency　of　VP　algorithm　to　estimate　parameters,　proposes　an　effective　algorithm　for　solving　the　separable　nonlinear　least　squares　problem　-　EM-VP　algorithm.　The　numerical　experiments　show　that　the　EM-VP　algorithm　can　select　the　appropriate　regularization　parameters,　so　that　the　estimated　model　has　better　generalization　performance.　©　2020　IEEE.