张勤 meaning in Chinese
qin zhang
Examples
- Dynamic causality diagram ( dcd ) is an uncertainty reasoning method proposed by professor zhang qin in 1994
动态因果图是由张勤教授于1994年提出的不确定推理方法。 - This paper mainly deals with the research of the probability knowledge expressing method and reasoning computation principle about dynamic causality diagram brought forward by pro . qingzhang
本论文主要对于张勤教授所提出的动态因果图的概率知识表达方式和推理计算原理进行了深入探讨。 - Due to the possible change of the coefficients in real world , qinqin zhang and zhan zhou considered the permanence of the nonautonomous two - species competition model of lotka - volterra type with delays , and the necessary and sufficient conditions for the permanence were obtained
进一步，由于系数可能会改变，张勤勤和周展考虑了二维非自治时滞lotka - volterra竞争系统的持久性，而且获得了此系统持久性的充分必要条件。 - 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。 - The theory , which is found on and brought forward by pro . qin zhang is the causality trees / diagram ' s knowledge representation methodology and calculating theory based on probability . the research bases itself upon building own knowledge right for our country in ai software and changing the situation which we just follow the outside research in ai
本项研究基于张勤教授所提出的因果图的概率知识表达方式和推理计算原理，立足于建立具有我国自主知识产权和特色的不确定知识表达和推理的理论模型和工具，发展具有我国自主知识产权的人工智能软件产业，改变我国在人工智能领域跟踪国外研究的状况。