燒結(jié)料加水混合過(guò)程智能控制研究

1、大連輕工業(yè)學(xué)院碩士學(xué)位論文燒結(jié)料加水混合過(guò)程智能控制研究姓名：孫樂(lè)申請(qǐng)學(xué)位級(jí)別：碩士專(zhuān)業(yè)：機(jī)械設(shè)計(jì)及理論指導(dǎo)教師：金偉20040401AbstractInthecraftworkofmetallurgyusingthesintermineasamajormaterialoftheblastfumacepuddlingcallimprovethesmeltefficiencygreatlyconsequentlytheproductoft

2、hesinterminebecomesoneofthemostimportanttacheofthesteelproducecmftworkIntheprocessofsinterminefeedwithwateraimingtothedifficultywearefacinginthewaterfeedingautocontrol，wemademeasurestotheparametersandvariablesanddataproc

3、essing，thenadoptedtheneuralnetworkstoestablishamodelofthisprocess，thentookestimatestoprocessparameterswithinheritednervenetwork，anddidseriesofexploringresearchincludingthesimulationoftheautocontr01Sowecandemonstratethati

4、tisadvisabletoadoptaptitudecontroltechniqueasasolutionfortheproblemofthemeasureandcontrolwhichcontainsmanycomplexnon—linearityprocesseswithsomedisturbingfactorsThroughdisposalsofcomputerdatastrainwaveanddatascout，wesolve

5、dtheproblemofclosingtimesynchronizationinthedistributingmeasuredataacquiredalargequantityofpmcessparameter船modelsforneuralnetworksmodelingWithmanyimprovedmeasures，weaccomplishedtheprocessmodelingofnerve—network，comparedt

6、othetraditionalmodeling，it’SmuchclosertotheactualprocessIntheprocessofbuildingtheparameterestimatemodel，weadoptgeneticalgorithmtosearchthebestsolutioninthewholespaceforinitializationprocessofneuralnetworks，accordingtothe

7、experimentsdemonstratethatwecanconquertheproblemwhichitiseasytogetintothetroublesofthebestsolutioninancertainspaceduringthenettrainingsconsequentlythedatafromtheparameterestimatemodelismoreclosertotheactualcraftworkparam

8、eters，SOmakethequantityofwaterismoreclosertotheactualmeasureddata，itmadetheoreticfoundationsandexperimentaldependencefortheworkoftheactualexploitationandapplicationsKeyword：modeIingneuraInetworksgenetiOaIgorithm，themixed