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dc.contributor.author施明辉
dc.contributor.author周昌乐
dc.date.accessioned2017-11-14T03:19:39Z
dc.date.available2017-11-14T03:19:39Z
dc.date.issued2007-09-15
dc.identifier.citation哈尔滨工业大学学报,2007,(09):152-156
dc.identifier.issn0367-6234
dc.identifier.otherHEBX200709036
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/156742
dc.description.abstract介绍了基于可信度的带权不确定性推理.阐述了用神经网络(ANN)如何实现基于可信度的带权不确定性推理.分析了这种神经网络的知识表示、结构及其不确定性推理方法.说明了如何利用这种神经网络并行地实现多规则的基于可信度的带权不确定性推理,以及如何解决冲突消解问题.采用这种神经网络结构的系统不仅能实现基于可信度的带权不确定性推理,而且具有自学习、并行推理、冲突消解、抗缺省等能力.采用的神经网络结构突破了传统神经网络结构的设计思想,对于扩展神经网络的应用具有参考价值.
dc.description.abstractAn approach to weighted reasoning under uncertainty with certainty factor(WRUCF) based on artificial neural network(ANN) was proposed.The related notions and inference method about WRUCF were presented.Then,the new ANN to implement the inference of WRUCF was proposed,and the details about the ANN were interpreted,such as its structure and knowledge representation,its schema of uncertain reasoning as well as its mechanisms for parallel reasoning and conflict eliminating.A system exploiting this approach can not only implement WRUCF,but also has the capabilities of self-learning,parallel reasoning,and conflict eliminating.
dc.description.sponsorship国家自然科学基金资助项目(60672018);; 国家高技术研究发展计划资助项目(2006AA01Z129);; 厦门大学科技创新基金资助项目(XDKJCX20063011)
dc.language.isozh_CN
dc.subject神经网络
dc.subject机器学习
dc.subject不确定性推理
dc.subject专家系统
dc.subject知识表示
dc.subjectartificial neural network
dc.subjectmachine learning
dc.subjectuncertain reasoning
dc.subjectexpert system
dc.subjectknowledge representation
dc.title一种用ANN实现带权不确定性推理的方法
dc.title.alternativeAn approach to weighted uncertain reasoning based on artificial neural network
dc.typeArticle


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