A　radial　basis　function　neural　network　(RBFNN),　with　a　strong　function　approximation　ability,　was　proven　to　be　an　effective　tool　for　nonlinear　process　modeling.　However,　in　many　instances,　the　sample　set　is　limited　and　the　model　evaluation　error　is　fixed,　which　makes　it　very　difficult　to　construct　an　optimal　network　structure　to　ensure　the　generalization　ability　of　the　established　nonlinear　process　model.　To　solve　this　problem,　a　novel　RBFNN　with　a　high　generation　performance　(RBFNN-GP),　is　proposed　in　this　paper.　The　proposed　RBFNN-GP　consists　of　three　contributions.　First,　a　local　generalization　error　bound,　introducing　the　sample　mean　and　variance,　is　developed　to　acquire　a　small　error　bound　to　reduce　the　range　of　error.　Second,　the　self-organizing　structure　method,　based　on　a　generalization　error　bound　and　network　sensitivity,　is　established　to　obtain　a　suitable　number　of　neurons　to　improve　the　generalization　ability.　Third,　the　convergence　of　this　proposed　RBFNN-GP　is　proved　theoretically　in　the　case　of　structure　fixation　and　structure　adjustment.　Finally,　the　performance　of　the　proposed　RBFNN-GP　is　compared　with　some　popular　algorithms,　using　two　numerical　simulations　and　a　practical　application.　The　comparison　results　verified　the　effectiveness　of　RBFNN-GP.