• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.89-94
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• 2010

The most important of rule-base system is the knowledge base that determines the power of rule-base system. The important form of this knowledge is how to descript kinds of rules. The Rule-Base System (RBS) has been using in many field that need reflect quickly change of business rules in management system. As far, when develop the Rule-Based System, we must make a rule engine with a general language. There are three disadvantage of in this developed method. First, while there are many data that must be processed in the system, the speed of processing data will become very slow so that we cannot accept it. Second, we cannot change the current system to make it adaptive to changes of business rules as quickly as possible. Third, large data make the rule engine become very complex. Therefore, in this paper, we propose the two important methods of raising efficiency of Rule-Base System. The first method refers to using the Relational database technology to process the rules of the Rule-Base System, the second method refers to a algorithm of according to Quine McCluskey formula compress the rows of rule table. Because the expressive languages of rule are still remaining many problems, we will introduce a new expressive language, which is Rule-Base Data Model short as RBDM in this paper.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.1003-1006
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• 2003

This paper proposed An auto fuzzy rule-base extraction method using genetic algorithm. The suggested method is an auto fuzzy rule-base extration method neither expert advise fuzzy rule-base nor trial and error fuzzy rule-base. In order to confirm the validity of proposed method, we have applicated dc motor control and confirmed effective.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.12
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• pp.9-18
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• 1991

In this Paper, a rule-based method for the phenomenon of Korean vocal sound change is proposed. This method could be used to solve a problem between symbolic(Hangul)and phonetic language(Korean) for the study of Korean speech processing. A rule on the phenomenon of vocal sound rearranged for the rule base with a end-consonents on the authority of standard pronunciation rule. The proposed rule base system is simplified by the implementation for the vocal sound change. Also, it is useful to create the data base with phonetic value for the Korean voice processing by syllable unit.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1642-1645
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• 2002

In this paper, I propose a measure of the status of a rule base that can be used to predict the degree of difficulty in the maintenace of a rule base.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.1
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• pp.37-45
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• 1990

In this paper, a knowledge base editor is presented as a supporting environment for an expert system building tool, OPS5. The knowledge base editor is especially useful for the fast and easy development of a knowledge base when the OPS5 production language is used. This knowledge base editor has some special facilities such as syntax and type checking, rule browsing and automatic bokkeeping. The syntax and type checking provides the facilities to find syntax and type errors in an edited knowledge base, respectively. The rule browsing facility offers various pattern matching schemes to see the causes and effects of a concerned rule. Automatic bookkeeping keeps the updated date and user name of a rule for the later reference whenever a user adds or changes a rule.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.75-80
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• 2001

Rule inconsistency is an important issue that is needed to be addressed while designing efficient and optimal fuzzy rule bases. Automatic generation of fuzzy rules from data sets, using machine learning techniques, can generate a significant number of redundant and inconsistent rules. In this study we have shown that it is possible to provide a systematic approach to understand the fuzzy rule inconsistency problem by using the proposed measure called the Commonality measure. Apart from introducing this measure, this paper describes an algorithm to optimize a fuzzy rule base using it. The optimization procedure performs elimination of redundant and/or inconsistent fuzzy rules from a rule base.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.145-150
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• 1997

In this paper is studied a method of fuzzy logic control based on possibly inconsistent if-then rules representing uncertain knowledge or imprecise data. In most cases of practical applications adopting fuzzy if-then rule bases, inconsistent rules have been considered as ill-defined rules and, thus, not allowed to be in the same rule base. Note, however, that, in representing uncertain knowledge by using fuzzy if-then rules, the knowledge sometimes can not be represented in literally consistent if-then rules. In this regard, when it is hard to obtain consistent rule base, we propose the weighted rule base fuzzy logic control depending on output performance using neural network and we will derive the weight update algorithm. Computer simulations show the proposed method has good performance to deal with the inconsistent rule base fuzzy logic control. And we discuss the real application problems.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.430-440
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• 1997

The knowledge repressenatation technique by production rule has been popular method to represent to represent the experts'dxpertise in expert systems.In this paper,we propose a method to verify the integrity of rule base.Proposcd method models rule base as a Petri net and utilizes the systematic strucutural properties of the petri net for berifi-cation.We describe the pricesure to check rule base at both local and global level intermal verification.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1998.06a
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• pp.47-56
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• 1998

Designing Trim die block is a complicated and time-consuming process heavily resting on the experience of the designer. To reduce design time and human errors, a knowledge base is used to automated the design process. In this paper, a framework of the Rule-based CAD System is presented for trim die block design consisting of a rule-base, design process control module and geometric modeler (CATIA). The rule-base includes design rules and know-how of design specialist. CATIA is used as the overall CAD environment and rule-base and design control modules are developed by C++ language with an interface to CATIA. Using the rule-base, the designer can explore alternating designs fast by changing design parameters and the part-list is automatically created which avoids miscommunication.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.