A Strategy of Dynamic Reasoning in Knowledge-Based System with Fuzzy Production Rules

Fuzzy production rules have been successfully applied to represent uncertainty in a knowledge-based system. The knowledge organized as a knowledge base is static. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a strategy to reflect the dynamic nature of a system when we make reasoning with a knowledge-based system.This paper proposes a strategy of dynamic reasoning that can be used to takes account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy rules. A degree of match (DM) between actual input information and antecedent of a rule is represented by a value in interval [0, 1]. Weights of relative importance of attributes in a rule are obtained by the AHP (Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected to reasoning in knowledge-based system with fuzzy rules.With the proposed reasoning procedure, a decision maker can take his judgment on the given decision environment into a static knowledge base with fuzzy rules when he makes decision with the knowledge base. This procedure can be automated as a pre-processing system for fuzzy expert systems. Thereby the quality of decisions could be enhanced.     

一种基于模糊决策树的运动预测机制

通过考虑环境约束及用户运动的随机性,设计了一种基于模糊决策树的运动预测机制.该机制在示例集的基础上,利用属性模糊化和模糊分类熵建立初始模糊决策树,由此生成模糊决策规则进行预测,根据变化情况适时对模糊决策树进行必要的维护.仿真研究表明,该机制预测准确率较高,预测开销较小,是可行和有效的.     

A SIMPLE DECISION RULE ON FUZZY EVENTS

Decision making in real problems is done in a fuzzy environment. Thus, Fuzzy-Bayes decision rules have been proposed to cope with a fuzzy state of nature. These decision rules are based on the probability of fuzzy events, or the possibility measure of fuzzy events. Furthermore, a decision rule based on fuzzy utility functions and the possibility distribution of fuzzy events are constructed. However, in these decision rules the fuzziness of the fuzzy expected utility is very big, because these decision rules are based on the extension principle for calculation of the fuzzy expected utility. In this article, avoiding the large fuzziness of the expected utility, we proposed a simple decision rule based on the representation interval of the possibility distributions of fuzzy events and the representation value of the fuzzy utility function. Further, we discuss the application of this simple decision rule to the decision problems, in which the decision maker obtains the one-peak symmetric possibility distribution of a state of nature and the one-peak symmetric membership functions of fuzzy events on a state of nature, by his or her knowledge and his or her belief.     

Developing a group decision support system based on fuzzy information axiom

Information axiom, one of two axioms of axiomatic design methodology which is proposed to improve a design, is used to select the best design among proposed designs. In the literature, there are a lot of studies related to using of information axiom for the solution of decision making problems. Moreover, applications of information axiom have been increasing day by day. However, calculation procedure of information axiom is not only incommodious but also difficult for decision makers. In this paper, a decision support system (DSS) based on fuzzy information axiom (FIA) is developed in order to make this decision procedure easy. The developed system consists of a knowledge base module including facts and rules, inference engine module including FIA and aggregation method, and a user interface module including entrance windows. The main aim of this study is to present a DSS tool to help the decision makers to solve their decision problems by modifying data-base of the program. In this paper, an application procedure will be presented based on the optimal selection of location for emergency service to illustrate the implementation procedure of the proposed model.     

A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques

In this research work, a novel framework for the construction of augmented Fuzzy Cognitive Maps based on Fuzzy Rule-Extraction methods for decisions in medical informatics is investigated. Specifically, the issue of designing augmented Fuzzy Cognitive Maps combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods is explored. Fuzzy cognitive maps are knowledge-based techniques which combine elements of fuzzy logic and neural networks and work as artificial cognitive networks. The knowledge extraction methods used in this study extract the available knowledge from data in the form of fuzzy rules and insert them into the FCM, contributing to the development of a dynamic decision support system. The fuzzy rules, which derived by these extraction algorithms (such as fuzzy decision trees, association rule-based methods and neuro-fuzzy methods) are implemented to restructure the FCM model, producing new weights into the FCM model, that initially structured by experts. Concluding, our scope is to present a new methodology through a framework for decision making tasks using the soft computing technique of FCMs based on knowledge extraction methods. A well known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection is presented to illustrate the application of the proposed framework and its functioning.     

粗糙集的过饱和多交叉口协同优化模型研究

为解决现有模糊智能控制方法仅适用于单交叉口非饱和状态,满足区域交通过饱和多交叉口信号协同联动控制的需要,提出了高峰时期主通道优化控制策略。在粗糙集知识推理基础上,构建了以多交叉口状态信息为条件属性,以绿灯延长方式、绿灯延长相位和绿灯延长时间3个参数为决策属性的多决策属性模糊控制模型。运用可辨识矩阵与属性频度的属性约简方法对模型进行约简,提取决策规则。实例分析表明:多交叉口主通道绿灯时间延长3~8 s能够有效提高区域交通整体通行效能,同时延长时间不仅与过饱和状态车辆最大排队长度有关,还与绿灯延长方式、绿灯延长相位存在关联,这与交警经验总结的控制规律一致。     

一种基于精简的模糊规则库的分类算法*

结合模糊聚类和粗糙集提出了一种基于精简的模糊规则库分类算法.对于数值型样本数据,首先采用模糊聚类生成模糊规则库,然后运用粗糙集理论对样本属性进行约简,删除冗余规则,即可得到精简的模糊规则库,以方便进行分类决策.通过对IRIS的仿真测试表明,本算法所产生的模糊规则不仅简单易懂,而且分类效果很好.     

A Fuzzy Petri Nets approach for railway traffic control in case of abnormality: Evidence from Taiwan railway system

Railway traffic control by dispatchers in case of abnormality is critical to assure the service quality of a railway system’s operation. However, this unique professional knowledge often lies in the dispatcher’s mind. Therefore, this study aims to transform a train dispatcher’s expertise into a useful knowledge rule. The fuzzy Petri Net approach is adopted to formulate the decision rules of train dispatchers in case of abnormality as the basis for future development of a dispatching decision support system. The dispatching decision rules, factors, and possible options when perturbation happens are collected via expert interviews and literature reviews. This study discusses the abnormal scenarios, including centralized traffic control system failure, automatic train protection failure, and locomotive failure. A case study of a line section of Taiwan’s railway network is implemented and the empirical result could be used as a reference in railway dispatching in case of abnormality.     

AN INTELLIGENT DECISIONMAKER FOR OPTIMAL CONTROL

We present an expert system that can handle various complicated decision-making problems of optimal control that are hardly solvable manually or even by computer-aided design techniques. The expert system is the first implementation of solving decision-making problems of optimal control using a computer, which paves the way for us to develop the real-time intelligent optimal control environment. Through a user-friendly interface, the expert system can receive the needed information from the user, perform heuristic search, and provide the results of a decisionmaker quickly both on the screen and from a printer. The important features of this expert system are that it () makes a decision on the problem-solving strategies for optimal control, that is, provides the solution structure and transversality conditions as well as types of some key equations; (2) processes symbolic information; (3) breaks down the whole search into three reasoning levels such that the problem can be solved easily and the search routine can be simplified; (4) utilizes “filter rules” to reduce production rules and enhance the program efficiency; (5) modifies the knowledge base and creates new rules in production rule memory; and (6) applies a “certainty factor” to represent imprecise knowledge. The expert system has been implemented using the AI tool OPS5 on a VAX 11/780 computer running under VMS. An example is also used to illustrate our expert system.     

Fuzzy rule-based similarity model enables learning from small case bases

The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of “similarity” can vary in situations and is largely domain dependent. This paper proposes a novel similarity model consisting of linguistic fuzzy rules as the knowledge container. We believe that fuzzy rules representation offers a more flexible means to express the knowledge and criteria for similarity assessment than traditional similarity metrics. The learning of fuzzy similarity rules is performed by exploiting the case base, which is utilized as a valuable resource with hidden knowledge for similarity learning. A sample of similarity is created from a pair of known cases in which the vicinity of case solutions reveals the similarity of case problems. We do pair-wise comparisons of cases in the case base to derive adequate training examples for learning fuzzy similarity rules. The empirical studies have demonstrated that the proposed approach is capable of discovering fuzzy similarity knowledge from a rather low number of cases, giving rise to the competence of CBR systems to work on a small case library.     

A linguistic approach for the control of information flow in abattlefield environment

We developed three linguistic statements to describe user information desires in a battlefield information environment. These rules are based on end-user interest in each track report generated from radars across the battlefield. Along with these rules of user interest, a linguistic statement describing communications systems capabilities at each node was created. These linguistic statements were converted to fuzzy variables and these variables were used as network control devices in a simulation model. The model results show that effective communications control can be exercised by these simple rules     

Fuzzy rule-based reasoning approach for event detection and annotation of broadcast soccer video

This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach.     

Transferring Case Knowledge To Adaptation Knowledge: An Approach for Case-Base Maintenance

In this article we propose a case-base maintenance methodology based on the idea of transferring knowledge between knowledge containers in a case-based reasoning (CBR) system. A machine-learning technique, fuzzy decision-tree induction, is used to transform the case knowledge to adaptation knowledge. By learning the more sophisticated fuzzy adaptation knowledge, many of the redundant cases can be removed. This approach is particularly useful when the case base consists of a large number of redundant cases and the retrieval efficiency becomes a real concern of the user. The method of maintaining a case base from scratch, as proposed in this article, consists of four steps. First, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case base. Second, clustering of cases is carried out to identify different concepts in the case base using the acquired feature-weights knowledge. Third, adaptation rules are mined for each concept using fuzzy decision trees. Fourth, a selection strategy based on the concepts of case coverage and reachability is used to select representative cases. In order to demonstrate the effectiveness of this approach as well as to examine the relationship between compactness and performance of a CBR system, experimental testing is carried out using the Traveling and the Rice Taste data sets. The results show that the testing case bases can be reduced by 36 and 39 percent, respectively, if we complement the remaining cases by the adaptation rules discovered using our approach. The overall accuracies of the two smaller case bases are 94 and 90 percent, respectively, of the originals.     

Checking the coherence and redundancy of fuzzy knowledge bases

Checking the coherence of a set of rules is an important step in knowledge base validation. Coherence is also needed in the field of fuzzy systems. Indeed, rules are often used regardless of their semantics, and it sometimes leads to sets of rules that make no sense. Avoiding redundancy is also of interest in real-time systems for which the inference engine is time consuming. A knowledge base is potentially inconsistent or incoherent if there exists a piece of input data that respects integrity constraints and that leads to logical inconsistency when added to the knowledge base. We more particularly consider knowledge bases composed of parallel fuzzy rules. Then, coherence means that the projection on the input variables of the conjunctive combination of the possibility distributions representing the fuzzy rules leaves these variables completely unrestricted (i.e., any value for these variables is possible) or, at least, not more restrictive than integrity constraints. Fuzzy rule representations can be implication-based or conjunction-based; we show that only implication-based models may lead to coherence problems. However, unlike conjunction-based models, they allow to design coherence checking processes. Some conditions that a set of parallel rules has to satisfy in order to avoid inconsistency problems are given for certainty or gradual rules. The problem of redundancy, which is also of interest for fuzzy knowledge bases validation, is addressed for these two kinds of rules     

Intelligent data analysis with fuzzy decision trees

Intelligent data analysis has gained increasing attention in business and industry environments. Many applications are looking not only for solutions that can automate and de-skill the data analysis process, but also methods that can deal with vague information and deliver comprehensible models. Under this consideration, we present an automatic data analysis platform, in particular, we investigate fuzzy decision trees as a method of intelligent data analysis for classification problems. We present the whole process from fuzzy tree learning, missing value handling to fuzzy rules generation and pruning. To select the test attributes of fuzzy trees we use a generalized Shannon entropy. We discuss the problems connected with this generalization arising from fuzzy logic and propose some amendments. We give a theoretical comparison on the fuzzy rules learned by fuzzy decision trees with some other methods, and compare our classifiers to other well-known classification methods based on experimental results. Moreover, we show a real-world application for the quality control of car surfaces using our approach.     

GENERATING FUZZY RULES FROM RELATIONAL DATABASE SYSTEMS FOR ESTIMATING NULL VALUES

This paper presents a new algorithm for constructing fuzzy decision trees from relational database systems and generating fuzzy rules from the constructed fuzzy decision trees. We also present a method for dealing with the completeness of the constructed fuzzy decision trees. Based on the generated fuzzyrules, we also present a method for estimating null values in relational database systems. The proposed methods provide a useful way to estimate null values in relational database systems.