This　article　presents　a　novel　online　identification　algorithm　for　nonlinear　regression　models.　The　online　identification　problem　is　challenging　due　to　the　presence　of　nonlinear　structure　in　the　models.　Previous　works　usually　ignore　the　special　structure　of　nonlinear　regression　models,　in　which　the　parameters　can　be　partitioned　into　a　linear　part　and　a　nonlinear　part.　In　this　article,　we　develop　an　efficient　recursive　algorithm　for　nonlinear　regression　models　based　on　analyzing　the　equivalent　form　of　variable　projection　(VP)　algorithm.　By　introducing　the　embedded　point　iteration　step,　the　proposed　recursive　algorithm　can　properly　exploit　the　coupling　relationship　of　linear　parameters　and　nonlinear　parameters.　In　addition,　we　theoretically　prove　that　the　proposed　algorithm　is　mean-square　bounded.　Numerical　experiments　on　synthetic　data　and　real-world　time　series　verify　the　high　efficiency　and　robustness　of　the　proposed　algorithm.