A　novel　algorithm,　bases　on　the　adaptive　resonance　theory　(ART),　is　proposed　to　design　the　structure　of　radial　basis　function　(RBF)　neural　networks　in　this　paper.　Based　on　the　concept　of　＂similarity＂,　this　proposed　ART-like　algorithm　can　be　utilized　to　construct　the　RBF　neural　network.　The　number　of　the　hidden　nodes　is　able　to　be　adjusted　in　the　learning　process.　Meanwhile,　the　activity　of　each　hidden　node　can　be　owned　through　the　initial　width　design　to　make　the　structure　compact.　Finally,　three　examples　are　employed　to　test　the　effectiveness　of　the　proposed　ART-like　RBF　(ART-RBF)　neural　network.　The　results　indicate　that　this　ART-RBF　neural　network　has　better　comparable　generalization　performance　with　compact　structure　and　fast　training　time.