基于范例推理的税收案例分析系统设计

基于范例推理技术是专家系统实用而成熟的技术。对税收案例分析而言,其应用在实用性方面优于基于规则的系统。提出基于范例推理技术和应用XML来表示和搜索范例,应用数据仓库构建税收案例分析系统,详细介绍了系统的基于范例推理流程、相似性度量函数、范例相似匹配方法及范例维护的设计思路和实现步骤。利用基于范例推理技术,大大提高了系统的“智能”性和实际功能,在实际应用中产生了较好的经济效益和社会效益。     

范例推理技术在银行客户信用评估中的应用

范例推理技术是人工智能领域中一种基于知识的问题求解和学习方法。为了有效评估银行客户信用等级并提高银行信贷业务效率，文中提出了范例推理技术(CBR)在银行客户信用评估中的应用，并给出了基于范例推理的银行客户信用评估系统的原型，介绍了该系统中的关键技术：范例表示、相似性计算和范例检索，研究了归纳学习、特征子集选择等机器学习方法在范例检索中的应用。     

基于归纳技术的范例推理及其应用

首先研究了可以与范例推理相结合的多种技术，并着重研究了基于范例推理和归纳技术的集成方法，以充分利用范例推理和归纳技术的各自优势，提高求解问题的能力。该文提出了一个基于归纳技术的范例推理分类算法，实验证明了此算法有着良好的分类准确率。     

范例推理技术在银行客户信用评估中的应用

范例推理技术是人工智能领域中一种基于知识的问题求解和学习方法.为了有效评估银行客户信用等级并提高银行信贷业务效率,文中提出了范例推理技术(CBR)在银行客户信用评估中的应用,并给出了基于范例推理的银行客户信用评估系统的原型,介绍了该系统中的关键技术:范例表示、相似性计算和范例检索,研究了归纳学习、特征子集选择等机器学习方法在范例检索中的应用.     

基于数据挖掘和范例推理的智能分析决策支持技术综述

介绍了数据挖掘中的一些关键技术、人工智能基于范例推理、决策支持的主要理论及其发展,提出了范例推理、类比学习、规则推理之间的联系,详细探讨了数据挖掘技术、基于范例推理和决策支持理论集成的问题,最后对上述技术在预测领域的综合应用前景作了探讨。     

基于范例推理的CAD软件可重用技术的研究

基于软件重用技术与范例基技术在原理，在模具CAD领域软件的工具箱式CASE环境的研制中，通过范例基推理技术实现了CAD软件可重用机制，提出了CAD软件重用的范例定义，导出了范例推理算法，并给出范例推理可重用机制的功能模型。     

利用数据挖掘技术构造范例库的新方法探究

范例推理技术作为基于规则推理技术的补充,其关键就是能很好地解决知识获取的瓶颈问题,但在范例推理技术的实际应用中,如何高效建立范例库也是一个棘手的问题。采用数据挖掘技术,提出一种综合算法从传统数据库中构造范例库,可望部分解决范例获取的自动化问题,提高系统的运行效率及整体性能。     

范例推理在智能决策系统中的应用研究

范例推理是人工智能中重要的推理方法和机器学习技术,它也是智能系统中实用的技术之一。基于范例的决策是决策者认知心理的决策过程的一个合理描述,它提供了一种实现智能系统及决策的现实环境和技术方法。本文提出了基于范例推理的智能决策技术,给出应用模型,并进行了深入讨论。     

基于范例推理的CAD软件可重用技术的研究

基于软件重用技术与范例基技术在原理，在模具ＣＡＤ领域软件的工具箱式ＣＡＳＥ环境的研制中，通过范例基推理技术实现了ＣＡＤ软件可重用机制，提出了ＣＡＤ软件重用的范例定义，导出了范例推理算法，并给出范例推理可重用机制的功能模型。     

利用数据挖掘技术从气象数据库中建立范例库

基于范例的推理中的知识表示是以范例为基础，目前在基于范例的推理中，一个重要的任务是如何准备有效的范例库，在有些领域，过去虽然有比较详细的工作记录，建立了一些相关的数据库，但都是实际观测或工作运行状态的简单记录。我们采用了数据挖掘的技术来处理范例获取的问题，从存储于农业气象数据库中的信息发现范例，以形成范例库。     

Lyapunov based reasoning methods

Semiquantitative simulation is an approach for the analysis of uncertain dynamic systems that performs a comprehensive simulation study based on automated reasoning methods. Semiquantitative simulation of complex models is, however, hindered by the limited automated reasoning capabilities of the currently available semiquantitative simulation techniques. The paper describes the extension of semiquantitative simulation techniques on the basis of Lyapunov methods. This extension improves automated reasoning by utilizing generalized energy functions, called Lyapunov functions. Automated reasoning based on Lyapunov functions can be seen as a generalization of the energy considerations employed by engineers. It has the advantage that it can be used to analyze systems where it does not make sense to speak about energy in the physical sense. The difficult task of deducing a Lyapunov function for the semiquantitatively modeled dynamic system is solved by reformulating methods from nonlinear control theory. A procedure for an automatic deduction of a Lyapunov function and Lyapunov-based reasoning methods using this deduced Lyapunov function are given. The improved automated reasoning capabilities of our extended SQSIM simulation platform are demonstrated by example     

Universal triple I fuzzy reasoning algorithm of function model based on quotient space

Aiming at the deficiencies of analysis capacity from different levels and fuzzy treating method in product function modeling of conceptual design,the theory of quotient space and universal triple I fuzzy reasoning method are introduced,and then the function modeling algorithm based on the universal triple I fuzzy reasoning method is proposed.Firstly,the product function granular model based on the quotient space theory is built,with its function granular representation and computing rules defined at the same time.Secondly,in order to quickly achieve function granular model from function requirement,the function modeling method based on universal triple I fuzzy reasoning is put forward.Within the fuzzy reasoning of universal triple I method,the small-distance-activating method is proposed as the kernel of fuzzy reasoning;how to change function requirements to fuzzy ones,fuzzy computing methods,and strategy of fuzzy reasoning are respectively investigated as well;the function modeling algorithm based on the universal triple I fuzzy reasoning method is achieved.Lastly,the validity of the function granular model and function modeling algorithm is validated.Through our method,the reasonable function granular model can be quickly achieved from function requirements,and the fuzzy character of conceptual design can be well handled,which greatly improves conceptual design.     

基于FCM与模糊粗糙集理论的交通事件检测模型

为准确及时地发现高速公路上的事故隐患,有效地减少交通延误,保障道路安全,提出了一种新的基于模糊C均值（FCM）聚类和模糊粗糙集的交通事件自动检测模型。模型分为离散化、推理规则建立和模糊推理三个步骤。在属性离散化时,提出用常用的隶属度函数来拟合FCM聚类后的结果,并用此函数和参数来实现属性数据的离散化,避免了每次输入数据都必须通过聚类操作来进行离散化;采用了粗糙集理论建立推理规则,选择和交通事件密切相关属性并进行规则的约简,加速了模糊推理的速度;最后采用Max-Min模糊推理方法对交通事件进行检测。通过多种检测方法对比测试,结果表明了此模型在总体性能上优于传统的检测方法,验证了此模型的有效性,为交通事件的检测提供了一种新的思路。     

NEW METHODOLOGY TO FUZZY REASONING FOR RULE-BASED EXPERT SYSTEMS

Abstract

Much knowledge residing in the knowledge base of an expert system involves fuzzy concepts. A powerful expert system must have the capability of fuzzy reasoning. This paper presents a new methodology for dealing with fuzzy reasoning based on the matching function S. The single-input, single-output (SISO) fuzzy reasoning scheme and the multi-input, single-output (MISO) fuzzy reasoning schemes are discussed in detail. The proposed fuzzy reasoning methodology is conceptually clearer than the compositional rule of inference approach. It can provide an useful way for rule-based systems to deal with fuzzy reasoning.     

NFI: a neuro-fuzzy inference method for transductive reasoning

This paper introduces a novel neural fuzzy inference method-NFI for transductive reasoning systems. NFI develops further some ideas from DENFIS-dynamic neuro-fuzzy inference systems for both online and offline time series prediction tasks. While inductive reasoning is concerned with the development of a model (a function) to approximate data in the whole problem space (induction), and consecutively-using this model to predict output values for a new input vector (deduction), in transductive reasoning systems a local model is developed for every new input vector, based on some closest to this vector data from an existing database (also generated from an existing model). NFI is compared with both inductive connectionist systems (e.g., MLP, DENFIS) and transductive reasoning systems (e.g., K-NN) on three case study prediction/identification problems. The first one is a prediction task on Mackey Glass time series; the second one is a classification on Iris data; and the last one is a real medical decision support problem of estimating the level of renal function of a patient, based on measured clinical parameters for the purpose of their personalised treatment. The case studies have demonstrated better accuracy obtained with the use of the NFI transductive reasoning in comparison with the inductive reasoning systems.     

Fuzzy Reasoning as a Control Problem

Different from the dominant view of treating fuzzy reasoning as generalization of classical logical inference, in this paper fuzzy reasoning is treated as a control problem. A new fuzzy reasoning method is proposed that employs an explicit feedback mechanism to improve the robustness of fuzzy reasoning. The fuzzy rule base given a priori serves as a controlled object, and the fuzzy reasoning method serves as the corresponding controller. The fuzzy rule base and the fuzzy reasoning method constitute a control system that may be open loop or closed loop, depending on the underlying reasoning goals/constraints. The fuzzy rule base, the fuzzy reasoning method, and the corresponding reasoning goals/constraints define the three distinct ingredients of fuzzy reasoning. While various existing fuzzy reasoning methods are essentially a static mapping from the universe of single fuzzy premises to the universe of single fuzzy consequences, the new fuzzy reasoning method maps sequences of fuzzy premises to sequences of fuzzy consequences and is a function of the underlying reasoning goals/constraints. The Monte Carlo simulation shows that the new fuzzy reasoning method is much more robust than the optimal fuzzy reasoning method proposed in our previous work. The explicit feedback mechanism embedded in the fuzzy reasoning method does significantly improve the robustness of fuzzy reasoning, which is concerned with the effects of perturbations associated with given fuzzy rule bases and/or fuzzy premises on fuzzy consequences. The work presented in this paper sets a new starting point for various principles of feedback control and optimization to be applied in fuzzy reasoning or logical inference and to explore new forms of reasoning including robust reasoning and adaptive reasoning. It can be also expected that the new fuzzy reasoning method presented in this paper can be used for modeling and control of complex systems and for decision-making under complex environments.     

创新推理中的物元网络表示法

如何进行恰当地功能表示与创新推理是当前概念设计中的关键问题。融合语义网络法与物元表示法,提出物元网络的概念,在此基础上给出了物元网络表示法及其创新推理机制。通过与物元表示法、语义网络法的实验对比表明：物元网络法能更有效地进行功能表示、扩充与联想,从而更加有利于创新推理的开展。     

智能CAD专家系统开发平台的研究与实现

在开目二维和三维CAD的基础上，将专家系统的推理引擎嵌入CAD软件中，研究并开发出智能CAD开发平台，该平台将推理功能和参数化绘图功能融合在一起。另外该平台用到了自主开发的语言解释器（DPL），从而使得该平台具有较好的可扩展性和自适应性。通过该平台，我们在较短时间内开发出哈空调管束CAD专家系统，取得预期的目标。     

基于相似修正关系推理的焊接工艺决策

针对基于相似度的推理和合成关系推理存在的不足,本文提供一种将相似度量与贴近方向相结合,生成修正或诱导模糊关系的近似推理模式。通过引入模糊概念间贴近方向函数,构造扩展型和缩减型2类修正函数,由此导出推理模型的一般表达形式,并对几个修正算子和构造条件关系的模糊转化算子进行了分析比较。基于该推理模式构建焊接工艺决策模型,由给定熔深来确定合理的焊接规范参数,结果表明:模型可达到较高的计算精度,从而解决了近似推理中输出结果不能对输入事实的每一变化作出准确响应的问题。     

基于描述逻辑的推理系统设计与实现

语义Web的出现使得描述逻辑成为近期的研究热点,作为本体描述语言的基础,描述逻辑具有较强的表达能力.设计并实现了基于描述逻辑Tableaux算法的推理系统,实验结果表明,该系统可实现本体基本推理功能、TBox及ABox推理功能,且能融入到语义Web的实际应用系统中,减少对计算机的人工干预,在一定程度上提高了机器的理解能力.