This　paper　investigates　how　to　construct　a　recurrent　radial　basis　function　neural　network　(RRBFNN)　by　an　information-oriented　algorithm　(IOA)　and　how　to　adjust　the　parameters　by　a　gradient　algorithm　simultaneously.　In　this　IOA-based　RRBFNN　(IOA-RRBFNN),　the　proposed　IOA　is　used　to　calculate　the　information　processing　strength　(IPS)　of　hidden　neurons,　such　that　the　independent　component　contributions　between　the　hidden　neurons　and　output　neurons　can　be　extracted.　Then,　a　novel　self-organizing　strategy　is　proposed　to　optimize　the　structure　of　RRBFNN　based　on　the　input　IPS　and　output　IPS　of　hidden　neurons.　Meanwhile,　a　gradient　algorithm　is　developed　to　update　the　parameters　of　IOA-RRBFNN.　The　proposed　IOA-RRBFNN　can　be　used　to　organize　the　network　structure　and　adjust　the　parameters　to　improve　its　performance.　Finally,　several　examples　are　presented　to　illustrate　the　effectiveness　of　IOA-RRBFNN.　The　results　demonstrate　that　the　proposed　IOA-RRBFNN　is　more　competitive　in　solving　the　nonlinear　system　modeling　problems　compared　with　some　existing　methods.