In　this　paper,　a　self-organizing　fuzzy-neural-network　with　adaptive　computation　algorithm　(SOFNN-ACA)　is　proposed　for　modeling　a　class　of　nonlinear　systems.　This　SOFNN-ACA　is　constructed　online　via　simultaneous　structure　and　parameter　learning　processes.　In　structure　learning,　a　set　of　fuzzy　rules　can　be　self-designed　using　an　information-theoretic　methodology.　The　fuzzy　rules　with　high　spiking　intensities　(SI)　are　divided　into　new　ones.　And　the　fuzzy　rules　with　a　small　relative　mutual　information　(RMI)　value　will　be　pruned　in　order　to　simplify　the　FNN　structure.　In　parameter　learning,　the　consequent　part　parameters　are　learned　through　the　use　of　an　ACA　that　incorporates　an　adaptive　learning　rate　strategy　into　the　learning　process　to　accelerate　the　convergence　speed.　Then,　the　convergence　of　SOFNN-ACA　is　analyzed.　Finally,　the　proposed　SOFNN-ACA　is　used　to　model　nonlinear　systems.　The　modeling　results　demonstrate　that　this　proposed　SOFNN-ACA　can　model　nonlinear　systems　effectively.