Based　on　the　systemic　investigation　on　the　feedforword　neural　network,　for　the　problem　of　the　structure　design　of　the　RBF　neural　network,　a　new　flexible　structure　design　method　is　used　for　RBF　neural　network　in　this　paper.　By　computing　the　output-information　(OI)　of　the　hidden　neurons　and　the　multi-information　(MI)　of　the　hidden　nodes　and　output　nodes,　the　hidden　nodes　in　the　RBF　neural　network　can　be　inserted　or　pruned,　thus　the　topology　of　the　network　can　be　modulated.　This　method　can　effectively　solve　the　structure　design　of　the　RBF　neural　network.　The　grad-descent　method　for　the　parameter　adjusting　ensures　the　exactitude　of　the　flexible　RBF　neural　network　(F-RBF).　The　structure　of　the　RBF　neural　network　is　self-organizing,　and　the　parameters　are　self-adaptive.　In　the　end,　the　proposed　F-RBF　is　used　for　approximating　the　classical　non-linear　functions　and　modelling　key　parameters　of　the　wastewater　treatment　process.　The　results　show　that　the　F-RBF　obtains　a　favorable　dynamic　character　response　and　the　approximating　ability.　Especially,　comparied　with　the　minimal　resource　allocation　networks　(MRAN),　the　generalized　growing　and　pruning　RBF　(GGAP-RBF)　and　the　self-organizing　RBF　(SORBF),　the　proposed　algorithm　is　more　effective　in　terms　of　training　time,　generalization,　and　neural　network　structure.