• Journal of Digital Convergence
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• v.13 no.3
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• pp.155-165
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• 2015

Bankruptcy prediction has been one of the important research topics in finance since 1960s. In Korea, it has gotten attention from researchers since IMF crisis in 1998. This study aims at proposing a novel model for better bankruptcy prediction by converging three techniques - support vector machine(SVM), fuzzy theory, and genetic algorithm(GA). Our convergence model is basically based on SVM, a classification algorithm enables to predict accurately and to avoid overfitting. It also incorporates fuzzy theory to extend the dimensions of the input variables, and GA to optimize the controlling parameters and feature subset selection. To validate the usefulness of the proposed model, we applied it to H Bank's non-external auditing companies' data. We also experimented six comparative models to validate the superiority of the proposed model. As a result, our model was found to show the best prediction accuracy among the models. Our study is expected to contribute to the relevant literature and practitioners on bankruptcy prediction.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.143-151
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• 2001

The purpose of this study is to present the model for introduction of pharmacy information systems after medical reform. AHP(analytic hierarchy process) method is used to compute preference over factors which are included in the introduction of information systems. The fuzzified mu1ti-objective programming model is given to consider the aspects of resource and to accommodate the aspiration level and satisfaction level of decision makers. Numerical examples illustrating interpolated model are presented to accommodate the uncertainty of priority and the implications of this model is discussed.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.236-242
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• 2006

세계적으로 각 분야에서 SERVQUAL 모형과 SERVPERF 모형 등을 이용한 서비스품질에 대한 측정과 관련된 연구들이 많이 수행되어 오고 있지만 서비스품질을 계량화시키기 위한 연구는 활성화되고 있지 못하는 상황이다. 따라서, 본 연구의 목적은 불확실하고 주관적인 환경에서 서비스품질을 객관성있게 측정하고 계량화시키기 위해서 L.A. Zadeh가 제안한 퍼지이론의 Triangular Fuzzy Number(TFN) 와 T.L. Saaty가 제안한 Analytic Hierarchy Process (AHP)를 이용하여 서비스품질을 측정하기 위한 방법을 제안하는 것이고, 본 연구를 통해서 조직의 제한적 자원으로 고객만족 극대화를 실현하기 위한 경쟁우위적 전략의 일환으로써 서비스품질을 제고시키는데 효율적이며 효과적인 의사결정안이 도출될 것으로 사료된다.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.490-496
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• 1997

This paper describes a new process-plan selection method using a modified Fuzzy Quantification Theory(FQT). The problem of process-plan selection can be characterized by multiple attributes and used subjective, uncertain information. Fuzzy Quantification Theory is used for handling such information because it is a useful tool when human judgment or evaluation is quantified via linguistic variables, and the proposed method is concerned with the selection of a process plan by derivation of the values of categories for each attribute. In this paper, a modified Fuzzy Quantification Theory(FQT) is described and the procedure of this approach is explained and examples illustrated.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.4
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• pp.441-447
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• 2015

This paper proposes a novel fuzzy-based multi-criteria decision making method and implements a footstep planner for humanoid robots with it. Humanoid robots require additional footstep planning process in addition to path planning for the autonomous navigation. Moreover, it is necessary to consider safety and energy consumption as well as path efficiency and multi-criteria decision making is indispensable. The proposed method can provide not only well- distributed and non-dominated, but also more preferable solutions for users. The planned footsteps by the proposed method were verified through simulation. The results indicate that the user's preference is properly reflected in optimized solutions maintaining solution quality.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.251-254
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• 2003

데이터는 어떤 특성을 나타내는 언어적 또는 수치적 값들의 표현이다. 이러한 데이터들을 목적에 따라 구성한 것이 정보이며, 문제 해결이나 패턴 분류, 또는 의사 결정을 위해 정보들간의 관계를 규칙으로 체계화하는 것이 지식이다. 현재 대부분의 산업 분야에서 시스템에 대한 이해를 높이고 시스템의 성능을 향상시키기 위해 지식을 추출하고, 적용시키는 작업들이 활발히 이루어지고 있다. 지식 정보의 추출은 지식의 획득, 표현, 구현의 단계로 구성되며 이렇게 추출된 지식 정보는 규칙으로 도출된다. 본 논문에서는 여러 산업 분야에 걸쳐 다양하게 적용되는 지식 정보 추출 방법들에 대해 그 영역별로 알아보고 여러 시험 데이터들과 실제 시스템에 클러스터링(CL), 입력공간 분할(ISP), 뉴로-퍼지(NF), 신경망(NN), 확장 행렬(EM) 등의 방법들을 적용시킨 결과들을 비교 분석하고자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.29-32
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• 2008

Based on the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator and the interval-valued intuitionistic fuzzy weighted geometric (IIFWG) operator, we investigate the group decision making problems in which all the information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy numbers, and the information about attribute weights is partially known. A numerical example is used to illustrate the applicability of the proposed approach.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.275-285
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• 2004

This paper aims to construct an effective decision making model on selection of product design in product development using fuzzy AHP technique. It is expected that this paper contributes to enhancement of company's market competitiveness by shortening the lead time to develop a new product and minimize initial investment. The proposed model using fuzzy AHP enables quick decision making by integrating and analyzing all customer requirements related to a product. In addition, it can deal with vagueness and uncertainty of decision making process using fuzzy set theory. Decision making processes for evaluating the best selection of product design are also constructed to describe the exact concept of development. A tennis racket is shown as an example. The proposed model is expected to be applied in various fields of managerial decision making processes as well as of product development process.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.274-283
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• 2002

This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.177-179
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• 1993

Best generation mix belongs to a multi attribute decision making, because we should consider many uncertain factors and choose the best one of the alternatives. Sugeno's fuzzy integral has been used as the solution of multi attribute decision making, but the exact value of the coefficient $\lambda$ hat not known yet which is needed to calculate fuzzy measure. This paper proposes the new method to calculate fuzzy measure without fixing the value of $\lambda$. The proposed method considers $\lambda$ as a probability distribution function and calculated expectation of fuzzy measure. The method is applied to the test system and the validity of the method is verified.