模糊逻辑技术在医学诊断中的应用研究

介绍了不确定性推理技术中几种主要方法,并分析了各种方法的优缺点。根据目前医学诊断中存在的问题及不同医学诊断的各自特点,设计了基于模糊逻辑的计算机辅助医学诊断系统,主要介绍了其中采用的动态模糊逻辑和加权模糊逻辑相结合的方法。该方法不仅能够帮助医生初步确诊疾病,而且能够分析出引起某种疾病变化的主要原因,从而帮助医生提高业务水平,提高其诊断的速度,减少误诊率。     

模糊逻辑技术在医学诊断中的应用研究

介绍了不确定性推理技术中几种主要方法,并分析了各种方法的优缺点.根据目前医学诊断中存在的问题及不同医学诊断的各自特点,设计了基于模糊逻辑的计算机辅助医学诊断系统,主要介绍了其中采用的动态模糊逻辑和加权模糊逻辑相结合的方法.该方法不仅能够帮助医生初步确诊疾病,而且能够分析出引起某种疾病变化的主要原因,从而帮助医生提高业务水平,提高其诊断的速度,减少误诊率.     

三值关联规则在不确定性知识表示及推理中的应用

针对传统关联规则和 Ｂｏｏｌｅａｎ规则矩阵无法进行不确定性知识表示和推理的弱点,该文采用三值逻辑表示规则和真值状态的不确定性,提出并使用三值关联规则（Ｔ规则）和三值规则矩阵（ＴＲ矩阵）,利用 ＴＲ矩阵变换实现高速前向推理和后向推理。对复合三值规则的无损分解进行了详细探讨,最后提出了一个能进行不确定推理的新算法。     

证据理论在不确定性推理中的应用研究*

利用证据理论中的基本概率分配函数、信任函数和似然函数来描述和处理知识的不确定性。提出一个特殊的概率分配函数和新的组合规则,并以其为基础建立一个不确定性推理模型。实例证明该模型能有效地度量最终结论的不确定性。     

应急决策中基于时态知识的不确定性推理研究

针对应急决策中的不确定性,在传统区间代数方法的基础上,采用对区间时间断点模糊化处理并设定其取值范围的方法实现了应急领域不确定时态知识的表达,在此基础上,研究时态推理中的证据合成,通过时间区间集合,时态关系集合以及概率指派函数合成后的更新,给出了解决方案,结合应用算例进行分析。验证了该方法的有效性。     

基于规则的专家系统中不确定性推理的研究

提出了权值法和修正权值法两种不确定性推理算法,与常用的几种方法相比,权值法根据各证据重要程度的不同,区别对待证据的可信度信息,同时充分利用每一条信息;修正权值法除了具有权值法的优点外,又区分了可信度分布的差异。运用修正权值法已成功建造了多个实用专家系统。     

模糊空间Petri网的规则表示及动态推理

为便于表示模糊空间Petri网的状态变迁规则,根据空间关联影响区域分布现实特点,提出了空间模糊Petri网中的状态关联影响规则、变迁关联影响规则和多阈值激活规则。依据模糊产生式规则的特点,详细描述了10种具体的推理规则和表示组件,并以实例加以说明。在此基础上,结合模糊空间Petri网的特点提出了动态推理过程算法,可以实现各种空间状态规则因子的转化。动态推理的过程不仅可以获取某种“结果”,而且可以挖掘基于空间位置关联的中间状态及引起中间状态变化的事件,可以有效地指导风险过程预测和控制。     

模糊知识的不同否定及其在投资决策中的应用(英文)

在模糊知识中存在三种不同的否定,即矛盾否定、对立否定和中介否定,基于中介谓词逻辑MF与其无穷值语义解释Φ,研究了模糊知识及其三种否定的表示与推理,并在一个金融投资决策实例中进行了应用。引入了一种新的与Φ中参数λ相关的模糊产生式规则,讨论了实例中的模糊知识及其三种不同否定的推理算法与实现。     

金融投资决策中的模糊知识及其不同否定的表示与推理

基于一种带有矛盾否定、对立否定和中介否定的新模糊集FScom,研究了在一个金融投资决策实例中的应用.其中,对于模糊知识的不同否定,引入模糊集合～<'+>A和～<'->A,并采用距离比率函数思想定义了模糊集的隶属函数,给出了模糊集合FScom定义中λ值以及模糊产生式规则中周值τ的一种确定方法,讨论了实例中的模糊知识及其三种不同否定的推理算法与实现.     

基于DFL的缺省假设推理研究

日常生活中人们可以在信息不完全的情况下进行推理并得出较好的推理结论，而且在推理过程中，很多对象都是具有动态模糊性（DF Character）。因此文中针对研究对象以及它们之问的动态模糊性，提出了基于动态模糊逻辑（DFL）的缺省假设推理，并给出了缺省假设推理的框架描述、动态模糊（DF）知识的表示以及推理算法等。     

一种基于产生式规则的不确定推理模板模型的研究

该文针对现有不确定推理模型,结合专家知识不确定性在产生式规则系统中的体现,归纳出一种更接近人类专家处理不确定性和方便人们理解与构造实例模型的不确定推理模板模型。该模板模型可作为现有模型的分析模板和新的基于产生式规则的不确定推理模型研究与构造的基本框架模板。该文详细阐述了该模板模型的组成和工作原理,并用它对现有不确定推理模型进行了实例分析;最后,指出该模板模型各组成子模型的研究方向。     

A Default Logic Based Framework for Context-Dependent Reasoning with Lexical Knowledge

Lexical knowledge is increasingly important in information systems—for example in indexing documents using keywords, or disambiguating words in a query to an information retrieval system, or a natural language interface. However, it is a difficult kind of knowledge to represent and reason with. Existing approaches to formalizing lexical knowledge have used languages with limited expressibility, such as those based on inheritance hierarchies, and in particular, they have not adequately addressed the context-dependent nature of lexical knowledge. Here we present a framework, based on default logic, called the dex framework, for capturing context-dependent reasoning with lexical knowledge. Default logic is a first-order logic offering a more expressive formalisation than inheritance hierarchies: (1) First-order formulae capturing lexical knowledge about words can be inferred; (2) Preferences over formulae can be based on specificity, reasoning about exceptions, or explicit priorities; (3) Information about contexts can be reasoned with as first-order formulae formulae; and (4) Information about contexts can be derived as default inferences. In the dex framework, a word for which lexical knowledge is sought is called a query word. The context for a query word is derived from further words, such as words in the same sentence as the query word. These further words are used with a form of decision tree called a context classification tree to identify which contexts hold for the query word. We show how we can use these contexts in default logic to identify lexical knowledge about the query word such as synonyms, antonyms, specializations, meronyms, and more sophisticated first-order semantic knowledge. We also show how we can use a standard machine learning algorithm to generate context classification trees.     

基于DFL的缺省假设推理研究

日常生活中人们可以在信息不完全的情况下进行推理并得出较好的推理结论,而且在推理过程中,很多对象都是具有动态模糊性(DF Character)。因此文中针对研究对象以及它们之间的动态模糊性,提出了基于动态模糊逻辑(DFL)的缺省假设推理,并给出了缺省假设推理的框架描述、动态模糊(DF)知识的表示以及推理算法等。     

关于用VB实现知识推理的研究

专家系统核心部分也是难点部分是知识的表示与推理,为了更好地实现专家系统的诊断功能,在典型的故障诊断的各种征兆已广泛用于故障的判断情况下,利用VB和Access数据库,实现了故障的知识推理,可视性强。为故障预报和故障诊断模块提供了分析基础。     

三种新的基于相似性的加权模糊推理方法

在模糊专家系统中,模糊推理方法的优劣是衡量系统性能好坏的关键指标。基于相似性的加权模糊推理是针对模糊信息发展的一种既简单又灵活的方法,其关键是模糊集合相似度的定义。在文中,提出了三种新的相似度的定义,讨论了与其相应的模糊推理方法;通过实例验证了三种新方法的有效性,并与已有的几种方法进行了比较分析。     

Fuzzy operator logic and fuzzy resolution

There have been only few attempts to extend fuzzy logic to automated theorem proving. In particular, the applicability of the resolution principle to fuzzy logic has been little examined. The approaches that have been suggested in the literature, however, have made some semantic assumptions which resulted in limitations and inflexibilities of the inference mechanism. In this paper we present a new approach to fuzzy logic and reasoning under uncertainty using the resolution principle based on a new operator, the fuzzy operator. We present the fuzzy resolution principle for this logic and show its completeness as an inference rule.     

Reasoning with Sets of Defaults in Default Logic

We present a general approach for representing and reasoning with sets of defaults in default logic, focusing on reasoning about preferences among sets of defaults. First, we consider how to control the application of a set of defaults so that either all apply (if possible) or none do (if not). From this, an approach to dealing with preferences among sets of default rules is developed. We begin with an ordered default theory , consisting of a standard default theory, but with possible preferences on sets of rules. This theory is transformed into a second, standard default theory wherein the preferences are respected. The approach differs from other work, in that we obtain standard default theories and do not rely on prioritized versions of default logic. In practical terms this means we can immediately use existing default logic theorem provers for an implementation. Also, we directly generate just those extensions containing the most preferred applied rules; in contrast, most previous approaches generate all extensions, then select the most preferred. In a major application of the approach, we show how semimonotonic default theories can be encoded so that reasoning can be carried out at the object level. With this, we can reason about default extensions from within the framework of a standard default logic. Hence one can encode notions such as skeptical and credulous conclusions, and can reason about such conclusions within a single extension.     

Informational Logic as a Tool for Automated Reasoning

A logical entropy-based Informational Logic is presented which provides new tools for probabilistic automated reasoning and knowledge representation. Applications in automated theorem proving are examined, and a decision theory for probabilistic theorems is proposed.     

Bilattices and Reasoning in Artificial Intelligence: Concepts and Foundations

The past few decades have seen a resurgence ofreasoning techniques in artificial intelligenceinvolving both classical and non-classical logics. Inhis paper, ``Multi-valued Logics: A Uniform Approach toReasoning in Artificial Intelligence', Ginsberg hasshown that through the use of bilattices,several reasoning techniques can be unified under asingle framework. A bilattice is a structure that canbe viewed as a class of truth values that canaccommodate incomplete and inconsistent informationand in certain cases default information. Inbilattice theory, knowledge is ordered along twodimensions: truth/falsity and certainty/uncertainty. By defining the corresponding bilattices as truthspaces, Ginsberg has shown that the same theoremprover can be used to simulate reasoning in firstorder logic, default logic, prioritized default logicand assumption truth maintenance system. Although thisis a significant contribution, Ginsberg's paper waslengthy and involved. This paper summarizes some ofthe essential concepts and foundations of bilatticetheory. Furthermore, it discusses the connections ofbilattice theory and several other existingmulti-valued logics such as the various three-valuedlogics and Belnap's four-valued logic. It is notedthat the set of four truth values in Belnap's logicform a lattice structure that is isomorphic to thesimplest bilattice. Subsequently, Fitting proposed aconflation operation that can be used to selectsub-sets of truth values from this and otherbilattices. This method of selecting sub-sets oftruth values provides a means for identifyingsub-logic in a bilattice.