一般模糊极大-极小神经网络的研究与应用

分析一般模糊极大-极小神经网络的基本原理,阐述模糊计算方法在分类中的准确性和高效性。将一般模糊极大-极小神经网络应用于企业资信评估中,实现模糊区间的输入,缩小企业评估指标定量化中的误差范围。资信评估结果表明,该算法能快速、有效地对企业进行分类,为资信评估提供了解决方案。     

学分制下的班级文化建设

学分制是以选课为核心的新的统合教学管理制度。学会制下的班级,学生的流动性和不确定性比较明显,这给学生管理带来新的困难。班级文化作为一种柔性管理手段,具有强大的管理能力。班级文化建设,可以有效培养学生的集体主义精神,提高学生的自我管理能力。该文从人生观与世界观、寝室文化建设、班级活动和情感教育几方面对班级文化建设进行了论述,为解决这一困难提供了有益的思路。     

数字校园PKI／CA认证体系的规划和建设

文章首先简单分析了PKI／CA认证技术;接着分析了PKI／CA目前主流的三种建设模式,并结合校园的实际情况,提出了校园PKI／CA采用完全自建的模式,并采用层次信用体系结构,给出了具体的结构模型。最后对校园PKI／CA认证体系建设中会遇到的主要问题进行了简要分析。     

VRRP的基本概念及实施环境分析

在网络中终端主机通过配置缺省网关与外界连通,随着网络信息化的高速发展,各种应用系统渐渐深入人们的工作、生活,这时人们对主机与缺省网关之间的实时连通性提出了更高的要求。该文简单对VRRP在实际网络中的应用进行了分析,提出了实施建议,具有参考意义。     

A new methodology for query answering in default logics via structure-oriented theorem proving

We present a new approach to query answering in default logics. The basic idea is to treat default rules as classical implications along with some qualifying conditions restricting the use of such rules while query answering. We accomplish this by taking advantage of the conception of structure-oriented theorem proving provided by Bibel's connection method. We show that the structure-sensitive nature of the connection method allows for an elegant characterization of proofs in default logic. After introducing our basic method for query answering in default logics, we present a corresponding algorithm and describe its implementation. Both the algorithm and its implementation are obtained by slightly modifying an existing algorithm and an existing implementation of the standard connection method. In turn, we give a couple of refinements of the basic method that lead to conceptually different algorithms. The approach turns out to be extraordinarily qualified for implementations by means of existing automated theorem proving techniques. We substantiate this claim by presenting implementations of the various algorithms along with some experimental analysis.Even though our method has a general nature, we introduce it in the first part of this paper with the example of constrained default logic. This default logic is tantamount to a variant due to Brewka, and it coincides with Reiter's default logic and a variant due to ukaszewicz on a large fragment of default logic. Accordingly, our exposition applies to these instances of default logic without any modifications.     

科研信用标准化建设与体系框架研究

通过分析科研诚信与科研信用的基本属性和概念,提出科研信用体系建设的标准化需求,在此基础上,分析梳理了科研信用标准化的基本维度和分类,提出科研信用标准体系总体框架,并对科研信用标准化工作中的要点进行了分析和阐述,为推进我国科研信用标准体系建设提供参考.     

Abstract argumentation

In this paper we explore the thesis that the role of argumentation in practical reasoning in general and legal reasoning in particular is to justify the use of defeasible rules to derive a conclusion in preference to the use of other defeasible rules to derive a conflicting conclusion. The defeasibility of rules is expressed by means of non-provability claims as additional conditions of the rules.We outline an abstract approach to defeasible reasoning and argumentation which includes many existing formalisms, including default logic, extended logic programming, non-monotonic modal logic and auto-epistemic logic, as special cases. We show, in particular, that the admissibility semantics for all these formalisms has a natural argumentation-theoretic interpretation and proof procedure, which seem to correspond well with informal argumentation.In the admissibility semantics there is only one way for one argument to attack another, namely by undermining one of its non-provability claims. In this paper, we show how other kinds of attack between arguments, specifically how rebuttal and priority attacks, can be reduced to the undermining of non-provability claims.     

SAS判别分析在商业银行信用风险评估中的应用

针对我国商业银行传统客户风险评级体系现有的缺陷,介绍了如何把SAS判别分析方法应用于商业银行的客户信用风险评估,并进行验证分析,结果表明,SAS判别分析方法是一种高效,高准确度的评估方法.     

软件故障诊断方法浅析

软件故障诊断是软件开发和维护工作的一个重要组成部分,随着计算机软件技术的广泛应用,对软件系统性能及可靠性等方面的要求也越来越高。针对这个问题,基于工作经验,探讨了常用的软件故障诊断方法。     

贝叶斯网络分类模型研究及其在信用评估中的应用

基于概率估计的贝叶斯及贝叶斯网络分类模型,拥有其它数据挖掘工具所不具备的优势。在分析贝叶斯及贝叶斯网络分类模型基础上,结合最小风险决策准则,提出了一种新的信用评估模型。在实际数据集上采用交叉验证方式进行了测试。实验结果表明基于最小风险决策准则的贝叶斯及贝叶斯网络分类模型可以有效地减少信用评估风险。