专家系统中的同步解释和驾驭推理技术研究

专家系统中的解释一般是在推理结束后把推理结果和过程显示给用户，而不能在推理的过程中同步给出解释，并让用户主动地参与到推理过程中进行共同推理。因此，提出了一种可以实现同步解释和驾驭推理的交互式推理机(IRM：Interactive Reasoning Machine)模型。在推理过程中，一方面IRM可以把推理的过程和中间结果同步地显示给用户：另一方面用户可以直接参与到推理过程中，根据当前的推理信息修改当前使用的规则和证据以完成驾驭推理.     

遥感图像解释专家系统的研究与设计

在系统分析遥感图像解释专家系统（RSPES）的基本组成和改进方案的基础上,讨论了推理机制演算过程和知识库的构建,并描述了动态数据库的建立、图形图像先期处理过程、解释机构的建立、程序设计的方法等相关问题。在设计过程中,通过建立完备的知识库和基于产生式规则的搜索机制来支持模糊推理的实现,并将推理过程与解释机制结合,以此来提高系统工作效率,使系统能够更好地同用户进行交流和互动。     

专家系统外壳开发环境ESSDE

本文描述一个专家系统外壳开发环境ＥＳＳＤＥ，该环境支持用户选择不同的管理用户的基于规则的知识库机制来构造特殊的专家系统外壳。ＥＳＳＤＥ由若干ｍｅｔａ－ｉｎｔｅｒｐｒｅｔｅｒｓ组成，ｍｅｔａ－ｉｎｔｅｒｐｒｅｔｅｒｓ执行面向目标的推理机，若干解释机制，规则追踪和精确及不精确推理等不同的功能，ＥＳＳＤ还提供了“向用户提问”的界面和机制以及存储中间结论和重新评价结构的功能。     

基于案例的解释研究

解释是专家系统中一个重要环节,优秀的解释技术可以帮助用户增强使用系统的信心,帮助用户进一步理解系统的推理过程和结果.介绍基于案例解释的相关概念,然后从不同角度对近年来国内外研究进展做出综述与评价,并对基于案例的解释技术做出总结与展望.     

NChiql中基于语义依存树的语言转述

1.概述作为一个高效、鲁棒的自然语言查询接口系统(NLIDB),在进行了自然语言查询的分析之后,要有及时的反馈功能帮助用户理解系统的处理结果,避免用户对结果理解的偏差。具体来说,NLIDB的反馈功能,包括两个层次的含义:一是中间结果的反馈,即把系统对自然语言处理的中间结果(可以认为是系统对语言的一种理解)以某种用户可以接受的形式反馈给用户,常见的形式是把中间结果重新转换为自然语言,即转述(paraphrasi-ng);另一种是查询结果的反馈,即对查询结果作出明确的分析和解释,帮助用户理解系统的处理结果,我们称这一部分为结果语义分析。 NChiql在将自然语言查询转化为数据库查询语言之前,提供一套交互机制来确保系统对自然语言查询的理解和用户真正查询要求一致是极为必要的。 NChiql系统的自然语言理解过程可分为两阶段,即首先将自然语言转化为无歧义的中间形式——语义依存树,然后再将语义依存树转换成为SQL语句。     

基于不确定、不精确知识的推理系统─UKRS~＊ 

ＵＫＲＳ是建立在算子ＦＵＺＺＹ逻辑的λ－Ｈｏｒｎ集上，使用λ－ＳＬＤ归结推理机制作为其理论支持的一个基于不确定、不精确知识的推理系统。在ＵＫＲＳ中用λ－Ｈｏｒｎ子句表示知识，因算子ＦＵＺＺＹ逻辑对于算子是显式表示的，故可以方便的描述不确定、不精确知识，并且支持不确定、不精确推理。     

基于不确定,不精确知识的推理系统——UKRS

ＵＫＲＳ是建立在算子ＦＵＺＺＹ逻辑的λ－Ｈｏｒｎ集上，使用λ－ＳＬＤ归结推理机制作为其理论支持的一个基于不确定、不精确知识的推理系统。在ＵＫＲＳ中用λ－Ｈｏｒｎ子句表示知识，因算子ＦＵＺＺＹ逻辑对于算子是显式表示的，故可以方便的描述不确定、不精确知识，并且支持不确定、不精确推理。     

关系数据库在专家系统中的应用

在传统的专家系统中，对推理机工作过程的控制是程序实现的。本文介绍了一个用数据库技术引导推理过程的专家系统－精神分裂症诊断专家系统（ＥＳＳＤ）的推理控制策略。在这个专家系统中，作者将有关推理过程的控制信息用关系数据库技术进行存储和管理，达到了将控制策略数据化的目的。将数据库技术引入到专家系统的推进控制机制中可以使控制策略比较容易调整，且而可以大大压缩程序篇幅，显著提高系统运行效率，为专家系统的结构设     

化工过程通用故障诊断系统开发研究

介绍一个化工过程通用故障诊断系统ＣＰＤＢ，它采用面向对象技术实现知识表示及推理机制，其知识系统及推理系统都具有较强的重用性，可以 建立面向特定过程的实例系统，对某具体过程的仿真研究表明，ＣＰＤＳ的实例系统的建立及其在线运行结果令人满意。     

基于模糊用户模型的专家系统个性化解释方案研究

专家系统的解释是对用户提供一个关于系统行为的说明，从而提高系统的理解性和可接受性。系统中用户的知识水平存在很大的不同，合理的解释应该为不同的用户提供不同层次的解释。模糊用户模型(FUM，Fuzzy User Model)是解决这一问题的有力途径，系统可以通过它对用户的知识水平做出推理判断，得出用户需求从而产生合适的解释结果。文中提出了一种将FUM应用到专家系统中的方法，讨论了FUM对生成解释的影响，利用FUM构造个性化解释，并且对个性化解释方案作了一定的分析。     

A new approach to formulate fuzzy regression models

A fuzzy regression model is developed to construct the relationship between the response and explanatory variables in fuzzy environments. To enhance explanatory power and take into account the uncertainty of the formulated model and parameters, a new operator, called the fuzzy product core (FPC), is proposed for the formulation processes to establish fuzzy regression models with fuzzy parameters using fuzzy observations that include fuzzy response and explanatory variables. In addition, the sign of parameters can be determined in the model-building processes. Compared to existing approaches, the proposed approach reduces the amount of unnecessary or unimportant information arising from fuzzy observations and determines the sign of parameters in the models to increase model performance. This improves the weakness of the relevant approaches in which the parameters in the models are fuzzy and must be predetermined in the formulation processes. The proposed approach outperforms existing models in terms of distance, mean similarity, and credibility measures, even when crisp explanatory variables are used.     

An adaptive fuzzy information retrieval model to improve response time perceived by e-commerce clients

In this paper, an adaptive fuzzy logic-based information retrieval model is presented to enable users retrieve exact and specific information they sort after. The proposed IR model takes into consideration the limited bandwidth between ISP and its users; and the characteristics (processor speed, memory size, resolution, availability of anti-virus, etc.) of clients’ devices in ensuring that a customer has a fruitful and eventful session while conducting business online. The model was designed using unified modelling language and implemented using Borland JBuilder. A performance evaluation of the proposed information retrieval system using two evaluation measures was conducted. The experimental result indicated that the model has an acceptable performance.     

Success Factors for Using Case Management System in Italian Courts

This study takes a first step toward understanding the effectiveness of e-court applications in Italy. The proposed research model to assess the success factors for using case management system was tested in two Italian courts. Analysis of survey data from 314 users, using structural equation modeling, reveals the explanatory role of the model and the salient contribution of system and information quality variables on court staff’s performance. Our findings have important implications for theory and practice.     

Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust

Several approaches for recommending products to the users are proposed in literature, and collaborative filtering has been proved to be one of the most successful techniques. Some issues related to the quality of recommendation and to computational aspects still arise (e.g., cold-start recommendations). In this paper, we investigate the application of model-based Collaborative Filtering (CF) techniques and in particular propose a clustering CF framework and two clustering CF algorithms: Item-based Fuzzy Clustering Collaborative Filtering (IFCCF) and Trust-aware Clustering Collaborative Filtering (TRACCF). We compare several approaches by means of Epinions, MovieLens, Jester, and Poste Italiane datasets (with real customers). Experimental results show an increased value of coverage of the recommendations provided by TRACCF without affecting recommendation quality. Moreover, trust information guarantees high level recommendation for different users.     

Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection

With the rapidly growing number of available Cloud services, to fulfill the need for ordinary users to select accurate services has become a significant challenge. However, as a Cloud service environment encompasses many uncertainties that may hinder users to make sound decisions, it is highly desirable to handle fuzzy information when choosing a suitable service in an uncertain environment. In this paper, we present a novel fuzzy decision-making framework that improves the existing Cloud service selection techniques. In particular, we build a fuzzy ontology to model uncertain relationships between objects in databases for service matching, and present a novel analytic hierarchy process approach to calculate the semantic similarity between concepts. We also present a multi-criteria decision-making technique to rank Cloud services. Furthermore, we conduct extensive experiments to evaluate the performance of the fuzzy ontology-based similarity matching. The experimental results show the efficiency of the proposed method.     

一个基于流程的数据清洗框架的研究

以往的数据清洗方法需要基于模式进行规则编码,费时、困难,而且后期难以修改规则.提出了一种新的相似重复记录的消除框架,可以使用户在无需编码的条件下简单地完成数据清洗工作.该框架具有开放的算法库、函数库以及基于模糊规则和成员函数的模糊推导系统,使其具有较强的通用性和适用性.最后通过实验验证了该框架的有效性.     

个性化网站中用户分类的研究

用户分类是实现网站个性化的一项重要工作。我们从以往相同喜好的用户的访问内容、访问顺序中进行学习，经过综合、筛选后将其推荐给当前用户。这些推荐信息与用户兴趣间的相关度很高，能很大程度上满足用户的需求，主要讨论基于模糊聚类和相似优先的分类方法。     

Reversed fuzzy Petri nets and their application for fault diagnosis

An alterative approach to the backward reasoning is presented. In classical reasoning, both users and developers of many expert systems are dedicated to the forward reasoning. However, in many newly arising expert systems such as various diagnosis systems, the backward reasoning is of special interest and often preferable. In this paper, the fuzzy Petri nets are used to analytically represent the knowledge of fault diagnosis in manufacturing systems and an iterative algorithm based on max-algebra is used to deduce the consequence–antecedent relationship between their manifestation and antecedent. Finally, the legitimacy and efficiency of the proposed approach are proved and validated by an illustrative example.     

Detection of phishing attacks in Iranian e-banking using a fuzzy–rough hybrid system

Phishing is a method of stealing electronic identity in which social engineering and website forging methods are used in order to mislead users and reveal confidential information having economic value. Destroying the trust between users in business network, phishing has a negative effect on the budding area of e-commerce. Developing countries such as Iran have been recently facing Internet threats like phishing, whose methods, regarding the social differences, may be different from other experiences. Thus, it is necessary to design a suitable detection method for these deceits. The aim of current paper is to provide a phishing detection system to be used in e-banking system in Iran. Identifying the outstanding features of phishing is one of the important prerequisites in design of an accurate system; therefore, in first step, to identify the influential features of phishing that best fit the Iranian bank sites, a list of 28 phishing indicators was prepared. Using feature selection algorithm based on rough sets theory, six main indicators were identified as the most effective factors. The fuzzy expert system was designed using these indicators, afterwards. The results show that the proposed system is able to determine the Iranian phishing sites with a reasonable speed and precision, having an accuracy of 88%.     

HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems

Recommender Systems (RS) have been being captured a great attraction of researchers by their applications in various interdisciplinary fields. Fuzzy Recommender Systems (FRS) is an extension of RS with the fuzzy similarity being calculated based on the users’ demographic data instead of the hard user-based degree. Based upon the observations that the FRS researches did not offer a mathematical definition of FRS accompanied with its algebraic operations and properties, and the fuzzy similarity degree is not enough to express accurately the analogousness between users, in this paper we will present a systematic mathematical definition of FRS including theoretical analyses of algebraic operations and properties and propose a novel hybrid user-based fuzzy collaborative filtering method that integrates the fuzzy similarity degrees between users based on the demographic data with the hard user-based degrees calculated from the rating histories into the final similarity degrees in order to obtain high accuracy of prediction. Experimental results on some benchmark datasets show that the proposed method obtains better accuracy than other relevant methods. Lastly, an application for the football results prediction is given to illustrate the uses of the proposed method.