A　dynamic　T-S　(Takgi-Sugeno)　fuzzy　Elman　network　(DTSFEN)　,　which　has　the　advantages　of　the　T-S　fuzzy　model　and　Elman　network,　is　proposed　This　DTSFEN　can　process　dynamic　information　with　a　globally　convergent　recursive　structure　Moreover,　an　adaptive　error　back-propagation　learning　algorithm　is　designed　to　update　the　structure　parameters　and　the　fuzzy　rule　parameters,　which　can　improve　the　learning　efficiency　of　DTSFEN　Then,　the　global　convergence　of　the　DTSFEN　is　proven　using　the　Lyapunov　stability　theorem　Finally,　the　DTSFEN　is　used　for　nonlinear　function　approximation　and　sludge　volume　index　(SYT)　soft　sensor　The　experimental　results　show　that　the　DTSFEN　has　a　faster　convergence　rate　and　better　accuracy　and　robustness　than　the　orthogonal　least　squares　(OLS)　and　Elman　network.