基于模糊认知图的贝叶斯信度网构造方法分析-analysis of bayesian belief network construction method based on fuzzy cognitive map.docx
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基于模糊认知图的贝叶斯信度网构造方法分析-analysis of bayesian belief network construction method based on fuzzy cognitive map
AbstractArtificialintelligenceisanintelligentbehaviorthatcomputerimitatesthehumanbrain.Byartificialintelligence,thecomputercanhavetheabilityandtechnologysimilartohumanbeings.Ononehand,itcansummarizethemethodsofhumanintelligencebehaviorintothebasicprocess;ontheotherhand,itcanalsocreateasuitableoperationforcomputersimulatingthehumanbrain.HowtoexpresstheexistentknowledgeandhowtoapplythemforanalysisandinferencetoobtainnewknowledgeisnowoneofkernelquestionsinAI.Inman-intelligentbehavior,theuseofcausionisoneofbasicintelligentbehavior.Andthen,asthebasisofartificialintelligenceresearch,howtorepresentanddealwiththeuncertaintyofknowledgeisapuzzleofartificialintelligencemustbefacedwith.Fuzzycognitivemapandbayesianbeliefnetworkaretwomajorformalismsformodeling,representing,andreasoningaboutcausaldesignknowledge.Bothofthemodelsaregraphical,andtheyusenodesforrepresentingdomainvariablesanddirectedlinksbetweennodesforrepresentingrelationshipsofcause-and-effectbetweenvariables.Fuzzycognitivemapisacombinationproductofthefuzzylogicandneuralnetwork;itisalsoaveryconvenienttoolforsimulating,representingandstudyingthedynamicssystem.Ithasbeenshowntobeusefulintheintelligentdecision-making,managementscience,operationsresearchandotherfields.Bayesianbeliefnetworkshasasoundmathematicalfoundationofprobabilitytheory,itprovidesamodelofhumanreasoningandshowsanoutstandingabilityinuncertainknowledgerepresentationandreasoning.Thispaperintroducesthepresentresearchsituation,researchbackgroundandthebasicprincipleofthefuzzycognitivemapandbayesianbeliefnetwork,focusesontheprocessformodeling,representingandreasoningaboutcausalknowledgeofthistwoformalisms,andcomparestherolesofthembasedonvariousinherentfeaturessuchasusability,expressiveness,reasoningadequacy.Accordingtothecomparisonresultsbetweenfuzzycognitivemapandbayesianbeliefnetwork,fuzzycognitivemaphasbeenshowntobesimpler,moreintuitive,morehigh-level,andmoreuser-friendly.Thesefeaturesmakeitverysuitableforuseatthefront-endofknowledgeengineeringforacquisitio
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