The　presence　of　outliers　in　sample　data　can　corrupt　the　model＇s　performance,　giving　undesirable　results.　A　novel　adaptive　weighted　least　squares　support　vector　machine(AWLS-SVM)regression　method　is　presented　for　modeling　of　penicillin　fermentation　process.　In　AWLS-SVM,　least　square　support　vector　machine　regression　is　employed　for　the　sample　data　to　develop　model　and　obtain　the　sample　datum　fitting　error.　According　to　the　fitting　error,　the　adaptive　sample　weights　are　obtained　via　the　proposed　improved　normal　distribution　weighted　scheme.　The　hybrid　chaos　differential　evolution　simulated　annealing(CDE-SA)algorithm　is　proposed　to　obtain　the　optimal　parameters　of　the　model.　The　simulation　experiment　results　show　that　the　outliers　influencing　on　the　models　performance　is　eliminated　in　AWLS-SVM,　and　that　the　prediction　performance　is　better　than　those　of　least　squares　support　vector　machine(LS-SVM)and　weighted　least　squares　support　vector　machine(WLS-SVM)method.　The　AWLS-SVM　is　applied　to　develop　the　soft　sensor　model　for　penicillin　fermentation　process,　and　the　satisfactory　result　is　obtained.　©　2017,　Editorial　Department　of　Journal　of　Nanjing　University　of　Science　and　Technology.　All　right　reserved.