The　number　of　hidden　nodes　is　a　critical　factor　for　the　generalization　of　ELM.　Generally,　it　is　heavy　for　time　consumption　to　obtain　the　optimal　number　of　hidden　nodes　with　trial-and-error.　A　novel　algorithm　is　proposed　to　optimize　the　hidden　node　number　to　guarantee　good　generalization,　which　employs　the　PSO　in　the　optimization　process　with　structural　risk　minimization　principle.　The　simulation　results　indicate　our　algorithm　for　the　optimal　number　of　hidden　nodes　is　reasonable　and　feasible　with　6　datasets　on　benchmark　problems　by　the　accuracy　comparisons.　(C)　20112　The　Authors.　Published　by　Elsevier　Ltd.　Selection　and/or　peer　review　under　responsibility　of　American　Applied　Science　Research　Institute