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proof by contradiction相关的网络例句

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与 proof by contradiction 相关的网络例句 [注:此内容来源于网络,仅供参考]

The argument principle of the method of proof by contradiction is base on equivalent relation of dual propositions.

反证法是利用对偶命题等价的原理进行论证。

Based on this analysis, we then made a teaching assumption to get rid of such kind of thinking barrier, ie."Symbolizing the propositions in everyday life and finding dual proposition according to symbolic rule will enhance students' understanding of the argument principle of proof by contradiction."

进一步提出克服此种思维限制以发展逻辑思维的教学假设─「将生活情境中命题的真伪关系符号化,利用符号规则推理再转译回生活命题,可增进学生对反证法论证原理的了解」,并据以发展教案进行探究性教学。

Secondly,it is proved that monotonicity is equivalent to the law of proof by contradiction,and a simpler equivalent condition to monotonicity is given.

首先,讨论了择优逻辑的若干基本性质,并给出了单调性的一个充要条件;其次,证明了单调性与反证律等价,并给出了单调性的一个更为简洁的充要条件;最后,讨论了解释集合是归纳集时的单调性,证明了单调性与择优蕴涵的退化等价。

On the base of applied tech. and system structure, the key tech. are expressed in detail. It includes:Knowledge representation technology: it is described in two stages by "Depictive frame + Rule group"."Depictive frame" is used to reflect the factors concerning question field."Rule group" reflects the son-question in question field; knowledge management system: it is made up of knowledge-base editing component, factor classifying component, knowledge inspecting component and extractive knowledge component; uncertainty reasoning adopt method of elicitation search strategy and data drive controlled by member knowledge. It take reliability of compound proof at first. That can resolve the matching contradiction. Reasoning explanation introduce pre-text and followed method of resolving path. Knowledge discovery component take SLIQ as basic arithmetic. It provides classification model in two stage forms which aim at training suit. That is decision tree and productive rule.

在确定应用技术与系统结构的基础上,重点阐述了本文的核心技术,这包括:知识表示技术,采用"描述框架+规则组"的层次结构,"描述框架"用于描述问题域所涉及的因素,"规则组"反映问题域内的子问题;知识管理系统,由知识库编辑构件、因素分类构件、知识检测构件和知识求精构件组成;不确定性推理机采用启发式搜索策略,元知识控制的数据驱动,以复合证据可信度优先作为匹配冲突消解的策略,推理解释采用预置文本与求解路径追踪的方法;知识发现,采用SLIQ快速可伸缩算法,分类模型用判定树与产生式规则两种形式描述。

On the base of applied tech. and system structure, the key tech. are expressed in detail. It includes:Knowledge representation technology: it is described in two stages by "Depictive frame + Rule group"."Depictive frame" is used to reflect the factors concerning question field."Rule group" reflects the son-question in question field; knowledge management system: it is made up of knowledge-base editing component, factor classifying component, knowledge inspecting component and extractive knowledge component; uncertainty reasoning adopt method of elicitation search strategy and data drive controlled by member knowledge. It take reliability of compound proof at first. That can resolve the matching contradiction. Reasoning explanation introduce pre-text and followed method of resolving path. Knowledge discovery component take SLIQ as basic arithmetic. It provides classification model in two stage forms which aim at training suit. That is decision tree and productive rule.

在确定应用技术与系统结构的基础上,重点阐述了本文的核心技术,这包括:知识表示技术,采用&描述框架+规则组&的层次结构,&描述框架&用于描述问题域所涉及的因素,&规则组&反映问题域内的子问题;知识管理系统,由知识库编辑构件、因素分类构件、知识检测构件和知识求精构件组成;不确定性推理机采用启发式搜索策略,元知识控制的数据驱动,以复合证据可信度优先作为匹配冲突消解的策略,推理解释采用预置文本与求解路径追踪的方法;知识发现,采用SLIQ快速可伸缩算法,分类模型用判定树与产生式规则两种形式描述。

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