• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1457-1464
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• 2011

In this paper, we go one step further in that the number of competitive layers is not determined by experience but can be determined by fuzzy control rules based on input pattern information. In our method, we design a set of membership functions and corresponding rules and used Max-Min reasoning proposed by Mamdani. Also, we use centroid method as a defuzzification. In experiment that has various patterns of English inputs, this new method works beautifully to determine the number of competitive layers and also efficient in overall accuracy as a result.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.558-562
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• 2001

In this paper, a fuzzy hyperresolution principle called CFHR. Compensatory Fuzzy Hyperresolution, with positive compensation facility is proposed. Usually hyperresolution has several terms of conditon parts. Theser terms have to be connected by the an connective. If the main/max operator to be used the and operation, there is some dependency problem of the min/max operator. So , we propose a compensatory operator EGM and applied it to the CFHR, We show the CFHR does more meaningful reasoning than existing method. We also prove the completeness of CFHR.

• 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 Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.951-958
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• 1998

In this paper, we propose a backward reasoning algorithm which can be utilized in the fuzzy Petri-net representation representing fuzzy production rules. The fuzzy Petri-net representation can be used to model a approximate reasoning system and implement a fuzzy inference engine. The proposed algorithm, which uses the proper belief evaluation functions according to fuzzy concepts in antecedentes and consequents of fuzzy production rules, is more closer to human intuition and reasoning than other methods. This algorithm generates the backward reasoning path from the goal to the initial nodes and evaluates the belief value of the goal node using belief evaluation functions.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.