In　the　previous　work,　the　parameters　of　radial　basis　function　network　based　autoregressive　(RBF-AR)　models　are　estimated　offline　and　no　longer　updated　afterward.　In　this　letter,　an　adaptive　learning　algorithm　is　proposed　for　the　RBF-AR　models.　The　proposed　strategy　is　that　the　nonlinear　parameters　are　previously　determined　by　an　off-line　variable　projection　method;　and　once　new　samples　are　available,　the　linear　parameters　are　updated.　The　linear　adaptive　algorithm　adopted　in　this　letter　is　the　multi-innovation　least　squares　method,　due　to　its　high　performance.　The　simulation　results　show　that　with　the　adaption　of　the　linear　parameters,　the　prediction　performance　of　the　RBF-AR　models　may　be　significantly　improved,　which　demonstrates　the　effectiveness　of　the　proposed　algorithm.　©　2019　IEEE.