基于不确定性理论的企业风险动态管理方法研究

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1、厦门大学博士学位论文基于不确定性理论的企业风险动态管理方法研究姓名：纪崑申请学位级别：博士专业：统计学指导教师：曾五一20080301给出了企业风险状态空间的描述；通过建立风险因素特征指标的移动离差系数，来考察特征指标值在一段时期内的波动情况，从而实现对风险因素实时的、动态的识别；为了全面反映控制策略对风险因素的影响，兼顾定量与定性的信息，引入了风险控制策略的多维特性集及其功效函数；运用可拓理论通过构造关联函数来度量和评价控制策略的控制程度和控制效果，从而选择最优控制策略。5．根据企业所面临的复杂的投资决策中的问题，建立了多维多目

2、标模糊优选动态规划模型，并将遗传算法与动态规划法相结合进行求解，保证了优化变量的解的全局最优性。关键词：企业风险管理；预测；识别；综合评价AbstractEnterpriseriskmanagementistorecognize，weighandcontrolenterpriserisksinasystematicandstandardizedmannerSOastominimizeallpossiblelossescausedbyriskswithminimumcosts．Generally,intheactualmanagem

3、ent，thereexistmanyuncertaintiesnotonlyofrisksbutofsubjectiveorimprovisationalfactorsinmakingdecision．Inthiscase，indeterminacytheoryisusuallyemployedtoquantifyandmanagetheseuncertainties．Accordingly,onthebasisofindeterminacytheory,andinlinewithsystemtheory,controltheor

4、y,artificialintelligence，thisarticleaimsatputtingforwardsomedynamicmethods，whichmayhelpmanageenterpriseriskstechnologically．Therearefourchaptersinthisarticle．Inthefirstchapter,thesignificanceandtheoutlineofthestudyareintroduced．Inthesecondchapter,problemsofenterpriser

5、iskspredictionarepresentedandthepredictionmethodsofthetraitindicesareexaminedandresearched．Andinthethirdchapter,thedynamicrecognitionandtheevaluationmethodsareanalyzed．Finally,inthefourthandlastchapter,thedynamiccontrolandmethodsofmaltingdecisionsinenterpriseriskmanag

6、ementarestudied．f场atareinnovativeinthisarticleareasfollows：1．Greyneuralnetworkisproposedtopredictthetraitindicesofenterpriserisks．ThedatalimitsofGNNM(1，1)predictivemodelaretheoreticallypresentedandtheresultsherearetestifiedtobevalid．Therefore，GNNM(3，1)predictivemodeli

7、ssetupgroundedonGNNM(1，1)andprovedvalid．2．Consideringtheproblemsinrecognizingthelevelsofenterpriserisks,fuzzypattemrecognitionandneutralnetworkrecognitionarecombined，谢tlltheiradvantagescomplementary．nlecombinationofthesetworecognitionmethodsisprovedvalidthroughempiric

8、alanalysis．3．Inviewoftheevaluationindicesandrequirementsofenterpriserisks，andbasedonfuzzyoptimumtheoryandsystemtheory,amulti