uncertain knowledge造句

• Bayes network is a new inference and express method of uncertain knowledge
  摘要贝叶斯网络是不确定性知识表达与推理的一种新方法。
• Rough set theory and fuzzy set theory are two different mathematical methods for represent the uncertain knowledge
  粗糙集和模糊集是处理数据的两种不同的数学方法。
• Rough sets theory , an intelligent method for mining and processing imprecise and uncertain knowledge , has got great improvement
  粗糙集（ roughsets ）理论是一门日益成熟起来的用于不精确，不确定知识挖掘与处理的重要智能技术方法。
• The bayesian network ( bn ) proposed by pearl is a new mechanism for uncertain knowledge representation based on probability theory and graph theory
  贝叶斯网络( bayesiannetwork , bn )是pearl提出的一种基于概率论和图论的不确定知识表示模型。
• Evidence reasoning has perfect performance in expression of uncertain knowledge , which is the reason why it has been making great progress in theory and application in recent years
  证据推理在不确定性知识表示方面具有优良的性能，这是近几年其理论和应用发展较快的原因。
• Aiming at the uncertain knowledge and information in the design scheme decision - making , a multi - targets and factors system level gray correlation analysis model was established
  摘要针对设计方案决策中存在许多不确定的知识和信息问题，提出一种处理多层次、多因素客观信息的系统层次灰关联分析理论模型。
• Rough set theory was proposed by polish mathematician pawlak , which used to represent the uncertain knowledge . rough set theory has become a main method for kdd due to its unique advantage in knowledge discovery
  粗糙集合是波兰数学家pawlak提出的一种对不确定性知识的表示方法，粗糙集合理论凭借其独特的优势而在kdd领域中具有越来越重要的地位。
• Rough sets theory is a new mathematical tool , which analyses the facts hiding in data without any additional knowledge about the data , and a pithily tool for processing vague , noisy and uncertain knowledge
  粗糙集理论是一种新型的处理含糊和不确定性知识的数学工具,它能够分析隐藏在数据中的事实而不需要关于数据的任何附加知识,是处理含有噪声、不精确、不完整数据的有力工具。
• Be dead against to different field knowledge apply different inference strategy , apply different inference method to certain knowledge and uncertain knowledge , so make the design and evaluation inference more integrity
  针对甘蔗收获机械设计与评价专家系统领域知识的不同特点应用不同的推理策略，针对甘蔗收获机械的确定性知识和不确定性知识应用不同的推理方法，使得对甘蔗收获机械设计与评价推理更加完整。
• The rough set theory was put forward by professor pawlak , poland university of science and technology , as a method to study the expression and learning of uncomplete or uncertain knowledge base . it was attached importance by many scholars around the world
  Rough集(也称粗集)理论是由波兰华沙理工大学pawlak教授于20世纪80年代初提出的一种研究不完备、不确定知识库和数据的表达，学习，归纳的理论方法，近年来得到许多国际学者的重视。
• How to use advanced control technique on alkali recovery process is the key point of this paper . rough set theory is a newly developed mathematical tool for dealing with uncertain knowledge . it can be combined with other theories and has great effects
  但是碱回收过程的控制自动化水平还不高，如何在碱回收过程中应用先进控制技术正是本文研究的重点，而粗糙集作为一种新型的处理不确定性知识的数学工具，可以结合现有的理论，发挥重要的作用。
• This dissertation discusses and studies to surround the knowledge representation , learning , reasoning , and the main contents include : at the first chapter , some familiar uncertain knowledge representation and reasoning and the difficulties of them : evidential theory , certainty factor , fuzzy logic and fuzzy reasoning , subjective bayesian method , belief network are introduced . we present the basic knowledge , primary reasoning algorithm , complexity of reasoning algorithm , the way of dealing with some problem of causality diagram relative and the research direction in causality diagram theory particular at the second chapter
  论文围绕着因果图的知识表达、学习、推理进行了讨论和研究,主要内容包括:在扼要介绍了一些比较常见的不确定性知识的表示和推理方法:证据理论、确定性因子、模糊逻辑与模糊推理、主观bayes方法、信度网的基本知识之后,比较详细地阐述了因果图的知识表达,主要的推理算法、计算复杂度以及对一些问题的处理方式方法。
• Dynamic causality diagram was first proposed by professor zhang qin in 1994 , it is a mathematics tool combined with probability and graph theory , just like the belief network , its characteristic is to provide the method of uncertain knowledge representation and agility reasoning , it adopts nodes to represent random variables in the domain and directional edges between nodes to represent causal relationship between variables , linkage intensity to represent the strength of the link between these variables , it supports the forms of reasoning from cause to effect and from effect to cause and together
  动态因果图由张勤教授1994年提出,它与信度网类似,是概率论与图论结合的一种数学工具,其特点是提供不确定知识的表达和灵活的推理方法:用节点表示事件或变量,有向边表示因果关系,并用连接强度来表示因果关系的强度,支持由原因到结果的正向推理方式和由结果到原因的反向推理方式以及正反向混合推理方式。
• With the rapid development of computer network and communication technology , a great deal of information which comes from different domains is comprehended and accepted by people via computers . and along with the higher demand the captured information and the communication exchanges more frequently , the information semantic integration is needed in computers ’ sharing data . uncertainty knowledge becomes more prevalent in many application . probability theory has been proved to be one of the most powerful approaches to capture the degree of belief about uncertain knowledge . bayesian network ( bn ) , also called bayesian belief network , are widely - used approaches in probability theory at all times . with the development of the semantic web , ontology has become widely used to represent the conceptualization of a domain . ontology mapping has widely relied on the ontology semantic integration
  随着计算机网络和信息技术的高速发展,不同知识领域的大量信息需要人们通过计算机来理解和接受,而且随着人们对信息获取的要求越来越高,以及信息间数据交换的日益频繁,需要对计算机之间的信息交换进行语义级的合成。不确定性知识也得到了越来越普遍的应用和发展。概率理论已经被证明是获得非确定性知识的置信度的最有力方法之一。
• Then , it introduces the relative principle and main direction of research and developing methods , where relativity is deeply investigated . to emphasizes methods and treatment of knowledge operation , it also introduces the main conception and corresponding theory of rs ( rough set ) , where rs is compared with other uncertain knowledge reasoning . by using these principles , the article raises some new practicing algorithms , such as analyzing case base by utilizing rs ( data reduct etc . ) , index or retrieving by rs , discovering knowledge from case base , and domain knowledge using weight or weight extracting
  本文首先探讨了智能决策支持系统( idss )的基本原理、结构、构造方法、研究现状以及发展方向，对三种主要的智能决策支持系统的结构进行了广泛的比较研究；针对智能决策支持系统研究中所存在的问题，引出了基于案例推理( cbr ) ，介绍了基于案例推理的相关理论、工作机理，并对相似性进行了深入的探讨；接着介绍了粗糙集( rs )的主要概念和相关理论，主要介绍了对知识的处理方法和技术，重点介绍了粗糙集对知识的处理能力，并对粗糙集与其它的不精确推理进行了详细的比较。
• So , the fuzzy knowledge can be expressed with membership functions . this algorithm can deal with fuzzy causality diagram , which has multiple value , and effectively express the fuzzy uncertain knowledge in practice . after the introduction of the reasoning procedures about dynamic causality network ,
  本论文在介绍完动态因果图模型的推理过程后，对动态因果图在其发展过程中所遇到的一些困难做了归纳，并简要介绍了目前为此我们针对这些问题所取得的一些成就，以及以后的发展方向。
• * the belief network modeling of telecommunication network congestion the belief network is a mathematical tool based on both the probability and graph theory , which gives us an effective approach to represent uncertain knowledge and do flexible inference . it has been hot research topic in diagnosis , decision theory , ai , data mining and so on
  综合起来，主要研究了以下几方面的内容： *电信网阻塞的信度网模型信度网是概率论与图论完美结合的一种数学工具，其特点是提供不确定知识的表达和灵活的推理方法，在故障诊断、决策理论、人工智能、数据挖掘等方面成为研究热点。