基于神经网络的DCS系统可靠性容错控制

应用可靠性工程的基本知识，结合容错控制的原理，提出了一种DCS系统模型，以实现DCS系统的可靠性容错控制，并对神经网络建模的核心算法作出一定的推理和设想。     

自治故障管理系统推理规则的智能学习技术

随着高性能计算机系统规模急剧增加，系统整体的固有可靠性逐步降低，产生了“可靠性墙”问题。为了应对这一挑战，天河高性能计算机系统设计了自治故障管理系统，通过该系统实时监控、分析、管理全系统的报警、故障和错误。自治故障管理系统所收集的故障消息垂直涵盖系统的各个逻辑层次，水平覆盖系统的全部功能模块，因此故障消息之间存在逻辑上的因果关系，即一个故障源会导致后续一系列的故障事件。提出了一种针对于故障信息的推理规则学习算法EMRL,把故障信息的推理规则建模为一个概率模型，通过该模型自动从故障信息中挖掘故障推理规则，并且根据挖掘结果自动生成最小的故障推理图。采用天河系统的部分运行数据，验证了EMRL算法的有效性，结果表明EMRL能有效挖掘故障信息的推理关系。     

一个基于动态规划的规则引擎构件技术研究

提出了基于动态规划的规则引擎构件方案，推理采用基于动态规划的反向推理机制．推理时，先查找以前是否已作过的该推理，如果已作过，则不必重新推理，只需取结论；否则进行推理，并将中间结论保留下来，以便以后的推理建立在已有的推论之上，这样减少了推理的次数，极大的缩短了系统的响应时间．     

基于局部解释能力的贝叶斯网络隐藏变量学习

目前,在贝叶斯网络中插入隐藏变量的主要目的是简化贝叶斯网络结构,从而提高推理效率,但隐藏变量使用不当会降低推理的可靠性.本文以提高贝叶斯网络的局部最优解释推理能力为标准插入隐藏变量,并将最优解释、星形结构和Gibbs抽样相结合来确定隐藏变量的位置、取值和维数,因此,通过隐藏变量的引入,在能够提高推理效率的同时,还可改进推理的可靠性.     

冗余设计技术的有效性研究

冗余设计技术是提高产品可靠性水平的有效设计技术之一。但是究竟采用冗余设计能提高多少？冗余设计技术是否可以无限制的提高可靠性？在各种可靠性文献中未见有相关的推理和证明。作者在本文对上述问题做出了结论,并进行了数学推导与证明。     

计算机科学中的一些逻辑理论的推理性质研究

由于一阶 词逻辑的基本特征是具有可靠性,完全性,在推广一阶谓词逻辑表达能力及扩充其推理能力时,可以定义了了一些具有可靠性,完全性的逻辑,对此,在分析逻辑理论特征的基础上,给出了一些逻辑的推理系统之间相似性的实质,证明这些逻辑在实质上并不能扩充一阶谓词逻辑的推理能力,并指出非常单调推理中的缺省推理及限定推理所采用的技术是扩充时必需的。     

专家系统的可靠性

目前专家系统以其强大的掌握大量特定复杂系统的信息知识而得到广泛应用。但是，对于专家系统本身的可靠性问题必须引起足够的重视。本文就专家系统可靠性问题从４个方面进行分析：（１）专家系统软件可靠性；（２）专家系统知识可靠性；（３）专家系统推理过程可靠性；（４）可靠性与危险性。     

一种新的Petri网推理方法

提出一种新的基于Petri网的知识表达方法和逻辑推理算法，通过减少重复推理过程，充分利用推理过程中已经得到的中间结论，该算法比现有算法减少了推理步数，有效地提高了推理效率。     

递归数据传送进程的证明系统

本文提出了递归数据传送进程互模拟的证明系统，并证明了其可靠性和相对于数据推理的完备性，其中关键的推理规则是唯一不动点归纳法，这个结果一方面将Ｍｉｌｎｅｒ关于正则基本ＣＣＳ的公理系统推广到数据传送进程，另一方面将Ｈｅｎｎｅｓｓｙ与Ｌｉｎ关于有穷数据传送进程的证明系统推广到无穷进程。     

推理机制与推理方法

推理是人们在长期社会实践中，对客观事物的联系与关系的一种反映，可以从已有的知识推导出新的知识。所以，推理也是人类特有的智能活动，是逻辑演绎的过程。一般说来，推理包括两种判断，一种是已知的判断，称为前提；另一种是由已知的判断推出的新判断，称之为结论。一、推理的机制和推理的方法根据人脑思维方式上的特点，可以把推理分为演绎推理、归纳推理、类比推理等。演绎推理是一种充分置信报理，而归纳推理则不同，它是主观上不充分置信推理，当然也有一种可认为是部分地转移置信度的情况，而从推理过程的方向上，即前提与结论之间…     

Cataloguing/analogizing: A nonmonotonic view

Reasoning deductively under incomplete information is nonmonotonic in nature since the arrival of additional information may invalidate or reverse previously obtained conclusions. It amounts to apply generic default rules in an appropriate way to a particular (partially described) situation. This type of nonmonotonic reasoning can only provide plausible conclusions. Analogical reasoning is another form of commonly used reasoning that yields brittle conclusions. It is nondeductive in nature and proceeds by putting particular situations in parallel. Analogical reasoning also exhibits nonmonotonic features, as investigated in this paper when particular situations may be incompletely stated. The paper reconsiders the pattern of plausible reasoning proposed by Polya, “a and b are analogous, a is true, then b true is more credible,'' from a nonmonotonic reasoning point of view. A representation of the statement “a and b are analogous” in terms of nonmonotonic consequences relations is presented. This representation is then related to a logical definition of analogical proportions, i.e. statements of the form “a is to b as c is to d” that has been recently proposed and extended to other types of proportions. Remarkably enough, semantic equivalence between conditional objects of the form “b given a,” which have been shown as being at the root of nonmonotonic reasoning, constitutes another type of noticeable proportions. By offering a parallel between two important forms of commonsense reasoning, this paper enriches the comparison between nonmonotonic reasoning and analogical reasoning that is not often made. © 2011 Wiley Periodicals, Inc.     

直觉模糊近似推理中的可信度传播

针对直觉模糊近似推理中的可信度传播,提出计算结论可信度的相关算法。首先介绍了可信度因子的概念,分析了规则中的可信度因子传播对结论可信度的影响。然后针对直觉模糊近似推理的三种基本模式（取式、拒式、假言式）,给出了计算结论可信度值的相关公式,最后通过实例验证了算法的正确性。     

Understanding transfer pricing: a case-based reasoning approach

This paper introduces the domain of transfer pricing, followed by an overview of case-based reasoning. Then, case-based reasoning is used to analyse the results of a questionnaire survey in transfer pricing. Finally, some conclusions are drawn and opportunities for future research identified.     

Strategies in Human Nonmonotonic Reasoning

Although humans seem adept at drawing nonmonotonic conclusions, the nonmonotonic reasoning systems that researchers develop are complex and do not function with such ease. This paper explores people's reasoning processes in nonmonotonic problems. To avoid the problem of people's conclusions being based on knowledge rather than on some reasoning process, we developed a scenario about life on another planet. Problems were chosen to allow the systematic study of people's understanding of strict and nonstrict rules and their interactions. We found that people had great difficulty reasoning and we identified a number of negative factors influencing their reasoning. We also identified three positive factors which, if used consistently, would yield rational and coherent reasoning—but no subject achieved total consistency. (Another possible positive factor, specificity, was considered but we found no evidence for its use.) It is concluded that nonmonotonic reasoning is hard. When people need to reason in a domain where they have no preconceived ideas, the foundation for their reasoning is neither coherent nor rational. They do not use a nonmonotonic reasoning system that would work regardless of content. Thus, nonmonotonic reasoning systems that researchers develop are expected to do more reasoning than humans actually do!     

规则推理和案例推理的集成研究

基于规则的推理（ＲＢＲ）和基于案例的推理（ＣＢＲ）是知识或两类实用有效的推理方式，文中提出了一种ＲＢＲ与ＣＢＲ相互集成的方法，为消除ＣＢＲ和ＲＢＲ结果之间的冲突，引入了逆案例，隐含规则和相似性度量等概念，以此提高了系统求解问题的效率，增强了系统对不良结构问题的适应性。     

不确定规则推理在预测算法中的应用

文章针对海洋渔业资源的多种预测算法得到的结果集,提出了以框架的知识表示理论为基础的不确定规则推理预测系统修正算法,利用框架系统ＫＢＩＦ（Ｋｎｏｗｌｅｄｇｅ Ｂａｓｅ ｉｎ Ｆｒａｍｅ）的知识表示理论与黑板结构、不确定推理相结合对预测算法根据不同环境参数进行多种方式的修正,可以对系统预测结果进行有效的分析,以期得到更好的应用。     

Robustness of fuzzy reasoning and δ-equalities of fuzzy sets

Fuzzy reasoning methods (or approximate reasoning methods) are extensively used in intelligent systems and fuzzy control. In this paper the author discusses how errors in premises affect conclusions in fuzzy reasoning, that is, he discusses the robustness of fuzzy reasoning. After reviewing his previous work (1996), he presents robustness results for various implication operators and inference rules. All the robustness results are formulated in terms of δ-equalities of fuzzy sets. Two fuzzy sets are said to be δ-equal if they are equal to an extent of δ     

On nonmonotonic reasoning with the method of sweeping presumptions

Reasoning almost always occurs in the face of incomplete information. Such reasoning is nonmonotonic in the sense that conclusions drawn may later be withdrawn when additional information is obtained. There is an active literature on the problem of modeling such nonmonotonic reasoning, yet no category of method-let alone a single method-has been broadly accepted as the right approach. This paper introduces a new method, called sweeping presumptions, for modeling nonmonotonic reasoning. The main goal of the paper is to provide an example-driven, nontechnical introduction to the method of sweeping presumptions, and thereby to make it plausible that sweeping presumptions can usefully be applied to the problems of nonmonotonic reasoning. The paper discusses a representative sample of examples that have appeared in the literature on nonmonotonic reasoning, and discusses them from the point of view of sweeping presumptions.     

Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on the ranking values of fuzzy sets

Fuzzy interpolative reasoning is an important research topic of sparse fuzzy rule-based systems. In recent years, some methods have been presented for dealing with fuzzy interpolative reasoning. However, the involving fuzzy sets appearing in the antecedents of fuzzy rules of the existing fuzzy interpolative reasoning methods must be normal and non-overlapping. Moreover, the reasoning conclusions of the existing fuzzy interpolative reasoning methods sometimes become abnormal fuzzy sets. In this paper, in order to overcome the drawbacks of the existing fuzzy interpolative reasoning methods, we present a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the ranking values of fuzzy sets. The proposed fuzzy interpolative reasoning method can handle the situation of non-normal and overlapping fuzzy sets appearing in the antecedents of fuzzy rules. It can overcome the drawbacks of the existing fuzzy interpolative reasoning methods in sparse fuzzy rule-based systems.     

Unified full implication algorithms of fuzzy reasoning

This paper discusses the full implication inference of fuzzy reasoning. For all residuated implications induced by left continuous t-norms, unified α-triple I algorithms are constructed to generalize the known results. As the corollaries of the main results of this paper, some special algorithms can be easily derived based on four important residuated implications. These algorithms would be beneficial to applications of fuzzy reasoning. Based on properties of residuated implications, the proofs of the many conclusions are greatly simplified.