• Journal of the Korean Mathematical Society
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• v.45 no.1
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• pp.177-196
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• 2008

Using the results on the coefficients of Hermite-Fej$\acute{e}$r interpolations in [5], we investigate convergence of Hermite and Hermite-$Fej{\acute{e}}r$ interpolation of order m, m=1,2,... in $L_p(0<p<{\infty})$ and associated product quadrature rules for a class of fast decaying even $Erd{\H{o}}s$ weights on the real line.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.757-768
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• 2003

This paper proposes a weighted fuzzy reasoning algorithm for rule-based systems based on weighted fuzzy Pr/T nets, where the certainty factors of the fuzzy production rules, the truth values of the predicates appearing in the rules and the weights representing the importance of the predicates are represented by the fuzzy numbers. The proposed algorithm is more flexible and much closer to human intuition and reasoning than other methods : $\circled1$ calculate the certainty factors using by the simple min and max operations based on the only certainty factors of the fuzzy production rules without the weights of the predicates[10] : $\circled2$ evaluate the belief of the fuzzy production rules using by the belief evaluation functions according to fuzzy concepts in the fuzzy rules without the weights of the predicates[12], because this algorithm uses the weights representing the importance of the predicates in the fuzzy production rules.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.51-58
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• 2000

Discovery of association rules has been used useful in many fields, especially in the fields of the inventory display, catalog design and cross selling. In previous works, all transactions In the database are treated uniformly. In this paper, we present a method for partitioning transactions in the database using time weights. Transactions are assigned different weights as time goes on. Examples show that these method provides purchasing patterns in the database as well as finding association rules.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.143-146
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• 2004

In a situation that rank order information on attribute weights is captured, two solution approaches are presented. An exact solution approach via interaction with a decision-maker pursues progressive reduction of a set of non-dominated alternatives by narrowing down the feasible attribute weights set. In approximate solution approach, on the other hand, three categories of approximate methods such as surrogate weights method, the dominance value-based decision rules, and three classical decision rules are presented and their efficacies in terms of choice accuracy are evaluated via simulation analysis. The simulation results indicate that a method, which combines an exact solution approach through interactions with the decision-maker and the dominance value-based approach is recommendable in a case that a decision is not made at a single step under imprecisely assessed weights information.

• 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 the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.161-176
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• 2005

In a situation that ordinal preferences on multiattribute weights are captured, we present two solution approaches: an exact approach and an approximate method. The former, an exact solution approach via interaction with a decision-maker, pursues the progressive reduction of a set of non-dominated alternatives by narrowing down the feasible attribute weights region. Subsequent interactive questions and responses, however, sometimes may not guarantee the best alternative or a complete rank order of a set of alternatives that the decision-maker desires to have. Approximate solution approaches, on the other hand, can be divided into three categories including surrogate weights methods, dominance value-based decision rules, and three classical decision rules. Their efficacies are evaluated in terms of choice accuracy via a simulation analysis. The simulation results indicate that a proposed hybrid approach, intended to combine an exact solution approach through interaction and a dominance value-based approach, is recommendable for aiding a decision making in a case that a final choice is seldom made at single step under attribute weights that are imprecisely specified beyond ordinal descriptions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.210-214
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• 2002

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

• 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.

• 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.

• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1365-1372
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• 2001

We have suggested three learning rules and implemented an experimental system that automatically restructures hypertext networks according to their user browsing behavior. The system applies link weights to the hypertext networks and updates these weights according to the learning rules. The learning rules are based on how often a particular hyperlink is being traversed and operate on strictly local information of link traversals. Changes in network structure are fed back to users by dynamic link ordering according to descending link weight. This approach is used collaborative filtering and navigation support techniques. The system has been investigation to be able to structure random hypertext networks into valid representations of their user browsing preferences in experiments.