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1、Y12l”82洳；：j暗碩士學位論文⑨論文題目擔婪塞堡j金星基在詘經圈終蟲曲麈尉班囂作者姓名胡鬟堅指導教師翻窯焦蟊攫學科(專業(yè))蕉劐理盈生量到王鞋一所在學院。電置王捏堂囂AbstractRoughsettheoryisarelativelynewsoftcomputingtooltodealwi也vaguenessanduncertaintyIthasreceivedmuchattentionoftheresearchersaround

2、theworldRoughsettheoryhasbeenappliedtomanyareassugeessfullyincludingartificialintelligencepaRernrecognitioneto111ispaperplacesemphasisonthestudyofthebasicproblemsaboutroll吐settheoryknowledgereductionandrealvalHeattribute

3、sdiscretizationdiSGUSseSthegeneralizedrougllsetmodels，andproposeSthreedifferentmethodsinthreedifferentareasofcombiningroughsettheoryandneuralnetworktogetherbasedonthesetheoriesTIliSpaperintroducesthebasictheoryandconcept

4、ionofroughsetindetailAndthemaincontributionofthepaperinthe缸dlrleworkoftlleseasfollows：1111eproblemofknowledgereductioninrou吐settheoryAboveall，thispaperanalyzestheclassicmethodsbasedondiscemibilitymatrixThe／1thesignifican

5、ceofattributesindecisiontableiSdefinedfromtheviewpointofinformation；aheuristicalgorithmbasedoninformationentIoDvforreductionofknowledgeisproposed2nleproblemofrealValueattributesdiscretizationnliSpaperanalyzestheoriginalg

6、reedyalgorithmanditsimprovedalgorithm，integratestheadvantagesofseveralformeralgorithmsproposesusingmutualinformationtodefinethesignificanceofdiscretizationpointsindeeisiontable，andintegratestheconceptionofthecoreindiscre

7、tizationpointstoiudgingthesignificanceofdiscretizationpoints，thellbringsforwardsanewimprovedmethodabotltgreedyalgorithmbasedonmutualinformationsolvingthelimitationofformeralgorithmseffectively3Somegeneralizationareneeded

8、temedythelimitationofstandardrou吐setmodelsinpracticalapplicationherewein仃oducetwogeneralizedroughsetmodels：fuzzyroughsetandvariableprecisionroughsetmodelThetheoriesofthemhavebeenexpounded，andtheattributesreductionalgorit

9、hmsofthemhavebeenstudied4nlemethodsofintegrationofrou吐settheoryandneuralnetworktogetherhavebeenstudied，Tllispaperusingthesuperiorityofroughsetindealingwithimprecisionanduncertainty，makespretreatmenttodatasamples，reducest

10、heattributes，decreasesthedimensionsofsamples，getstheapproximationvalues，andobtainsthedecisionrulesafteroptimalreductionUsingtheserulestomantrainingsamplesofneuralnetworksconstructingthenumbersoflatentlayersandneuralcells

11、，makestheneuralnetworkmorelogical，reducesthe喇ningtimeofnetwork，andimprovestheprecisionoftrainingandtheabilityofgeneralizationAccordingtodifferentpatternsofpracticalapplicationwe掘ngforwardsdifferentmethodsofcouplingandalg

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