Aiming　at　the　problem　of　recurrent　radial　basis　function　(RRBF)　neural　network　structure　which　is　difficult　to　be　self-adaptive,　this　paper　proposes　a　structure　design　method　based　on　recursive　orthogonal　least　square　(ROLS)　algorithm.　Firstly,　ROLS　algorithm　is　used　to　calculate　the　contribution　and　the　loss　function　of　hidden　layer　neurons,　which　determines　to　increase　or　be　grouped　into　inactive　neurons,　and　the　topology　structure　of　neural　network　is　adjusted　accordingly.　At　the　same　time,　singular　value　decomposition　(SVD)　is　applied　to　determine　the　best　number　of　hidden　layer　neurons　in　order　to　delete　the　neurons　of　the　inactive　group,　which　effectively　solves　the　problems　of　RRBF　neural　network　structure　which　is　redundant　and　hardly　self-adaptive.　Secondly,　the　gradient　descent　algorithm　is　utilized　to　update　the　parameters　of　RRBF　neural　network　in　order　to　ensure　the　accuracy　of　neural　network.　Finally,　several　experiments　including　the　Mackey-Glass　time　series　prediction,　nonlinear　system　identification　and　key　water　quality　parameters　dynamic　modeling　in　wastewater　treatment　process　are　conducted,　and　the　simulation　results　prove　the　feasibility　and　effectiveness　of　the　structure　design　method.　©　All　Right　Reserved.