A　novel　growing-and-pruning　(GP)　approach　is　proposed,　which　optimizes　the　structure　of　a　fuzzy　neural　network　(FNN).　This　GP-FNN　is　based　on　radial　basis　function　neurons,　which　have　center　and　width　vectors.　The　structure-learning　phase　and　the　parameter-training　phase　are　performed　concurrently.　The　structure-learning　approach　relies　on　the　sensitivity　analysis　of　the　output.　A　set　of　fuzzy　rules　can　be　inserted　or　reduced　during　the　learning　process.　The　parameter-training　algorithm　is　implemented　using　a　supervised　gradient　decent　method.　The　convergence　of　the　GP-FNN-learning　process　is　also　discussed　in　this　paper.　The　proposed　method　effectively　generates　a　fuzzy　neural　model　with　a　highly　accurate　and　compact　structure.　Simulation　results　demonstrate　that　the　proposed　GP-FNN　has　a　self-organizing　ability,　which　can　determine　the　structure　and　parameters　of　the　FNN　automatically.　The　algorithm　performs　better　than　some　other　existing　self-organizing　FNN　algorithms.