• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.81-95
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• 2000

A knowledge-based system with fuzzy production rules is a representation of static knowledge of an expert. 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 real system when we make inferences with a knowledge-based system. This paper proposes a strategy of dynamic inferencing for a knowledge-based system with fuzzy production rules. The strategy suggested in this paper applies weights of attributes of conditions of a rule in the knowledge-base. A degree of match(DM) between actual input information and a condition of a rule is represented by a value [0,1]. Weights of relative importance of attributes in a rule are obtained by AHP(Analytic Hierarcy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with MIN operator, into a single DM for the rule. In this way, overall DM for a rule changes depending on the importance of attributes of the rule. As a result, the dynamic nature of a real system can be incorporated in an inference with fuzzy production rules.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.559-566
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• 2004

In general, the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions appearing in the rules are represented by real values between zero and one. If it can allow the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions to be represented by interval -valued fuzzy sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner. This paper presents fuzzy Petri nets and proposes an interval-valued fuzzy backward reasoning algorithm for rule-based systems based on fuzzy Petri nets Fuzzy Petri nets model the fuzzy production rules in the knowledge base of a rule-based system, where the certainty factors of the fuzzy propositions appearing in the fuzzy production rules and the certainty factors of the rules are represented by interval-valued fuzzy sets. The algorithm we proposed generates the backward reasoning path from the goal node to the initial nodes and then evaluates the certainty factor of the goal node. The proposed interval-valued fuzzy backward reasoning algorithm can allow the rule-based systems to perform fuzzy backward reasoning in a more flexible and human-like manner.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.625-631
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• 2004

In general, the certainty factors of the fuzzy production rules and the certainty factors of fuzzy Propositions appearing in the rules are represented by real values between zero and one. If it can allow the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions to be represented by interval-valued fuzzy sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner(15). This paper presents a fuzzy Petri nets and proposes an interval-valued fuzzy reasoning algorithm for rule-based systems based on fuzzy Petri nets. Fuzzy Petri nets model the fuzzy production rules in the knowledge base of a rule-based system, where the certainty factors of the fuzzy Propositions appearing in the furry production rules and the certainty factors of the rules are represented by interval-valued fuzzy sets. The proposed interval-valued fuzzy set reasoning algorithm can allow the rule-based systems to perform fuzzy reasoning in a more flexible manner.

• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.55-74
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• 2000

A knowledge-based with production rules is a representation of static knowledge of an expert. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a method to reflect the dynamic nature of a system when we make inferences with a knowledge-based system. This paper suggests a strategy of dynamic inference that can be used to take into account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy production rules. A degree of match(DM) between actual input information and a condition of a rule is represented by a value [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 in an inference with fuzzy production systems.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.2
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• pp.40-45
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• 2005

This paper has presented an application of an optimum grinding conditions based on the fuzzy logic. Fuzzy logic can handle vague and uncertain knowledge, and presents a scheme for integrating data with various kinds of grinding data. Especially, this research is capable of determining the grinding conditions taking into account some fuzzy membership function represented for trapezoidal form such as hardness and surface roughness of workpiece, material tensile strength and elongation, and requirement of grinding method. Larsen's fuzzy production method utilizing the fuzzy production rule can be applied on the establishment of grinding conditions, and also the output value obtained by the center of gravity method can effectively utilize the optimum grinding conditions.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.115-124
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• 2004

This paper presents a weighted fuzzy backward reasoning algorithm for rule-based systems based on weighted fuzzy Petri nets. The fuzzy production rules in the knowledge base of a rule-based system are modeled by weighted fuzzy Petri nets, where the truth values of the propositions appearing in the fuzzy production rules and the certainty factors of the rules are represented by fuzzy numbers. Furthermore, the weights of the propositions appearing in the rules are also represented by fuzzy numbers. The proposed weighted fuzzy backward reasoning generates the backward reasoning path from the goal node to the initial nodes and then evaluates the certainty factor of the goal node. The algorithm we proposed can allow the rule-based systems to perform weighted fuzzy backward reasoning in more flexible and human-like manner.

• Journal of Information Technology Applications and Management
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• v.12 no.4
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• pp.1-12
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• 2005

In general, other conventional researches propose the fuzzy Petri net-based fuzzy reasoning algorithms based on the exhaustive search algorithms. If it can allow the certainty factors representing in the fuzzy production rules to use as the heuristic information, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more effective manner. This paper presents a fuzzy Petri net(FPN) model to represent the fuzzy production rules of a rule-based system. Based on the fuzzy Petri net model, a weighted fuzzy reasoning algorithm is proposed to Perform the fuzzy reasoning automatically, This algorithm is more effective and more intelligent reasoning than other reasoning methods because it can perform fuzzy reasoning using the certainty factors which are provided by domain experts as heuristic information

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.63-74
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• 2002

This paper presents an application result of on-line fault diagnosis system for glass melting furnace using a fuzzy expert system. Operators maintain the furnace using the furnace Knowledge and experience, which directly influence the furnace and glass product. Firstly, knowledge and experience is achieved and analyzed to implement the furnace Knowledge and experience into fuzzy expert system. The acquired Knowledges determined as a crisp rule or a fuzzy rule to expect its characteristics. And, a linear regression is used as the input of fuzzy rule to consider the exact knowledge of human operator. The fuzzy expert system is implemented with G2 which is an on-line expert system tool of Gensym Co. The application to a production furnace of Samsung-Corning Co. in Suwon shows successful results of proposed fuzzy expert system.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.783-790
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• 2002

This paper proposes a fuzzy Pr/T net representation of interval-valued fuzzy set reasoning, where fuzzy production rules are used for knowledge representation, and the belief of fuzzy production rules are represented by interval-valued fuzzy sets. The presented interval-valued fuzzy reasoning algorithm is much closer to human intuition and reasoning than other methods because this algorithm uses the proper belief evaluation functions according to fuzzy concepts in fuzzy production rules.

• Korean Journal of Cognitive Science
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• v.10 no.4
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• pp.51-62
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• 1999

This paper proposes a fuzzy Petri net representation to represent the fuzzy production rules of a rule-based expert system. Based on the fuzzy Petri net representation. we present a fuzzy reasoning algorithms which consist of forward and b backward reasoning algorithm. The proposed algorithms. which use the proper belief evaluation functions according to fuzzy concepts in antecedent and consequent of a fuzzy production rule. are more closer to human intuition and reasoning than other methods. The forward reasoning algorithm can be represented by a reachability tree as a kind of finite directed tree. The backward reasoning algorithm generates the backward reasoning path from the goal to the initial nodes and then evaluates the belief value of the goal node using belief evaluation functions.