In　this　paper,　a　self-organizing　radial　basis　function　(SORBF)　neural　network　is　designed　to　improve　both　accuracy　and　parsimony　with　the　aid　of　adaptive　particle　swarm　optimization　(APSO).　In　the　proposed　APSO　algorithm,　to　avoid　being　trapped　into　local　optimal　values,　a　nonlinear　regressive　function　is　developed　to　adjust　the　inertia　weight.　Furthermore,　the　APSO　algorithm　can　optimize　both　the　network　size　and　the　parameters　of　an　RBF　neural　network　simultaneously.　As　a　result,　the　proposed　APSO-SORBF　neural　network　can　effectively　generate　a　network　model　with　a　compact　structure　and　high　accuracy.　Moreover,　the　analysis　of　convergence　is　given　to　guarantee　the　successful　application　of　the　APSO-SORBF　neural　network.　Finally,　multiple　numerical　examples　are　presented　to　illustrate　the　effectiveness　of　the　proposed　APSO-SORBF　neural　network.　The　results　demonstrate　that　the　proposed　method　is　more　competitive　in　solving　nonlinear　problems　than　some　other　existing　SORBF　neural　networks.