Stationarity　is　fundamental　for　time-series　modeling　and　prediction.　In　this　article,　we　focus　on　the　radial　basis　function　network-based　autoregressive　(RBF-AR)　models　which　have　been　widely　used　in　practical　applications.　Compared　to　previous　work,　we　give　a　less-restrictive　sufficient　condition　for　the　asymptotic　stationarity　of　the　RBF-AR　model.　The　parameter　estimation　of　the　RBF-AR　model　is　converted　to　the　optimization　of　a　variable　projection　functional　with　constraints　of　stationarity　to　always　derive　a　stationary　model.　The　constrained　evolutionary　algorithm　is　used　to　solve　the　optimization　problem.　Numerical　results　demonstrate　the　effectiveness　of　the　proposed　method.