A　nonlinear　model　predictive　control　(NMPC)　scheme　is　developed　in　this　paper　based　on　a　self-organizing　recurrent　radial　basis　function　(SR-RBF)　neural　network,　whose　structure　and　parameters　are　adjusted　concurrently　in　the　training　process.　The　proposed　SR-RBF　neural　network　is　represented　in　a　general　nonlinear　form　for　predicting　the　future　dynamic　behaviors　of　nonlinear　systems.　To　improve　the　modeling　accuracy,　a　spiking-based　growing　and　pruning　algorithm　and　an　adaptive　learning　algorithm　are　developed　to　tune　the　structure　and　parameters　of　the　SR-RBF　neural　network,　respectively.　Meanwhile,　for　the　control　problem,　an　improved　gradient　method　is　utilized　for　the　solution　of　the　optimization　problem　in　NMPC.　The　stability　of　the　resulting　control　system　is　proved　based　on　the　Lyapunov　stability　theory.　Finally,　the　proposed　SR-RBF　neural　network-based　NMPC　(SR-RBF-NMPC)　is　used　to　control　the　dissolved　oxygen　(DO)　concentration　in　a　wastewater　treatment　process　(WWTP).　Comparisons　with　other　existing　methods　demonstrate　that　the　SR-RBF-NMPC　can　achieve　a　considerably　better　model　fitting　for　WWTP　and　a　better　control　performance　for　DO　concentration.