%0 Journal Article
%T doublehiddenlayerrbfprocessneuralnetworkbasedonlinepredictionofsteamturbineexhaustenthalpy
%A gonghuanchun
%J ÈÈÁ¦·¢µç
%P 36-40
%D 2014
%X inordertodiagnosetheuniteconomicperformanceonline,theradialbasisfunction(rbf)processneuralnetworkwithtwohiddenlayerswasintroducedtoonlinepredictionofsteamturbineexhaustenthalpy.thus,themodelreflectingcomplicatedrelationshipbetweenthesteamturbineexhaustenthalpyandtherelativeoperationparameterswasestablished.moreover,theenthalpyoffinalstageextractionsteamandexhaustfroma300mwunitturbinewastakenastheexampletoperformtheonlinecalculation.theresultsshowthat,theaveragerelativeerrorofthismethodislessthan1%,sotheaccuracyofthisalgorithmishigherthanthatofthebpneutralnetwork.furthermore,thismethodhasadvantagesofhighconvergencerate,simplestructureandhighaccuracy.
%K steamturbine
%K exhaustenthalpy
%K rbfprocessneuralnetworkwithtwohiddenlayers
%K onlineforecasting
%U http://rlfd.paperopen.com//oa/darticle.aspx?type=view&id=201407007