• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.485-490
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• 2004

In data representation, the clustering performs a grouping process which combines given data into some similar clusters. The various similarity measures have been used in many researches. But, the validity of clustering results is subjective and ambiguous, because of difficulty and shortage about objective criterion of clustering. The fuzzy clustering provides a good method for subjective clustering problems. It performs clustering through the similarity matrix which has fuzzy membership value for assigning each object. In this paper, for objective fuzzy clustering, the clustering algorithm which joins principal components analysis as a dimension reduction model with bayesian learning as a statistical learning theory. For performance evaluation of proposed algorithm, Iris and Glass identification data from UCI Machine Learning repository are used. The experimental results shows a happy outcome of proposed model.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1219-1232
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• 2004

Recently, the web sites, such as e-business sites and shopping mall sites, deal with lots of catalog image information and contents. As a result, it is required to support semantic-based image retrieval efficiently on such image data. This paper presents a semantic-based image retrieval system, which adopts XML and Fuzzy technology. To support semantic-based retrieval on product catalog images containing multiple objects, we use a multi-level metadata structure which represents the product information and semantics of image data. To enable semantic-based retrieval on such image data, we design a XML database for storing the proposed metadata and study how to apply fuzzy data. This paper proposes a system, generate the fuzzy data automatically to use the image metadata, that can support semantic-based image retrieval by utilizing the generating fuzzy data. Therefore, it will contribute in improving the retrieval correctness and the user's satisfaction on semantic-based e-catalog image retrieval.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.123-140
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• 2009

Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of outstanding performance of control and forecasting accuracy. ANFIS has capability to refine its fuzzy rules interactively with human expert. In particular, when we use initial rule structure for machine learning which is generated from human expert, it is highly probable to reach global optimum solution as well as shorten time to convergence. We propose metrics to evaluate interpretability of generated rules as a means of acquiring domain knowledge and compare level of interpretability of ANFIS fuzzy rules to those of C5.0 classification rules. The proposed metrics also can be used to evaluate capability of rule generation for the various machine learning methods.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.463-468
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• 2001

We propose a new similarity measure based on relative membership function (RMF). In this paper, the RMF is suggested to represent the relativity between fuzzy subsets easily. Since the shape of the RMF is determined according to the values of its parameters, we can easily represent the relativity between fuzzy subsets by adjusting only the values of its parameters. Hence, we can easily reflect the relativity among individuals or cultural differences when we represent the subjectivity by using the fuzzy subsets. In this case, these parameters may be regarded as feature points for determining the structure of fuzzy subset. In the sequel, the degree of similarity between fuzzy subsets can be quickly computed by using the parameters of the RMF. We use Euclidean distance to compute the degree of similarity between fuzzy subsets represented by the RMF. In the meantime, we present a new linguistic approximation method as an application area of the proposed similarity measure and show its numerical example.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.6
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• pp.46-54
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• 1998

This paper presents a method for designing robust fuzzy $H^{\infty}$ controllers which stabilize nonlinear systems with parameter uncertainty adn guarantee an induced $L_{2}$ norm bound constraint on disturbance attenuation for all admissible uncertainties. Takagi and Sugeno's fuzzy models with uncertainty are used as the model for the uncertain nonlinear systems. Fuzzy control systems utilize the concept of so-called parallel distributed compensation(PDC). Using a single quadratic Lyapunov function, the stability condition satisfying decay rate and disturbance attenuation condition for Takagi and Sugeno's fuzzy model with parameter uncertainty are discussed. A sufficient condition for the existence of robust fuzzy $H^{\infty}$ controllers is then presented in terms of linear matrix inequalities(LMIs). Finally, design examples of robust fuzzy $H^{\infty}$ controllers for uncertain nonlinear systems are presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.265-276
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• 2017

In this paper, a robust and more accurate trajectory tracking control method for a mobile robot is proposed using WIPDC(Weighted Integral Parallel Distributed Compensation) and T-S Fuzzy disturbance observer. WIPDC reduces the steady state error by adding weighted integral term to PDC. And, T-S Fuzzy disturbance observer makes it possible to estimate and cancel disturbances for a T-S fuzzy model system. As a result, the trajectory tracking controller based on T-S Fuzzy disturbance observer shows robust tracking performance. When the initial postures of a mobile robot and the reference trajectory are different, the initial control inputs to the mobile robot become too large to apply them practically. In this study, also, the problem is solved by designing an initial approach path using a path planning method which employs $B\acute{e}zier$ curve with acceleration limits. Performances of the proposed method are proved from the simulation results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.386-393
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• 2008

시간이 부족한 현대인과 보살핌이 부족한 고령화 인구의 증가로 인해 비교적 가벼운 질병을 방치해 더 큰 고통을 겪는 경우가 발생하여 직접 병원에 가지 않고 자신의 건강 상태를 파악할 수 있는 시스템의 개발이 필요하게 되었다. 하지만 질병의 특성상, 증상의 차이와 구분에 의해 같은 질병이라도 다른 치료와 예방이 필요하고 다른 질병으로 세부 도출될 가능성이 있다. 따라서 증상의 차이를 고려하지 않고 단순한 증상의 선택만으로 도출된 결과는 상황을 더욱 악화시킬 가능성이 있다. 본 논문에서는 ART2 알고리즘을 이용하여 질병을 도출하고 증상의 차이를 구분하기 위해서 애매한 증상의 정도를 퍼지 소속 함수로 표현하고 퍼지 추론 방법을 적용하여 더욱더 정확한 질병 상세를 도출 할 수 있는 개선된 자가진단 시스템을 제시한다. 본 논문에서 제안한 방법을 전문의에게 분석을 의뢰한 결과, 본 논문에서 제안된 자가진단 시스템 방법이 이전의 방법보다, 지능형 자가 보조 진단 시스템으로서 사용자에게 더욱 효과적인 도움을 줄 수 있다는 가능성을 확인하였다.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.7
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• pp.158-168
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• 1995

연삭숫돌 선택은 공작물제거량, 숫돌의 소모량, 표면정도 및 공작물의 물리적,화학적 특성을 고려하여 설정하는 것을 기본으로 한다. 연삭가공은 구성인자의 상호관계가 복잡하여 이중에서 어느한 요소만을 고려하여 설정하기 어렵고 정량적 기준 또한 정해져있지 않아 현실적으로는 숙련자의 정성적이고 경험적 지식에 따른 주관적 척도에 의존하는 것이 대부분이다. 또한, 연삭숫돌은 작업조건중에서 대량생산을 제외하고는 되도록이면 현장에 구비되어 있는 작업가능한 연삭숫돌 중에서 선택하는 것이 바람직하다. 따라서 본시스템에서는 이와같은 점을 고려하여 최적 연삭숫돌선택을 퍼지이론에 기초한 현장 숙련자의 지식을 활용하므로써 연삭숫돌선택의 효율성을 도모하는 동시에 현장에 연삭숫돌이 구비되지 않은 경우도 고려하여 연삭숫돌설정에 우선순위를 제시하므로써 작업자에 연삭숫돌선택의 유연성을 부여하였다. 또한, 실용성있는 전문가시스템의 구축을 위해 정성적이고 경험적인 지식의 활용을 위한 지식표현으로 설문조사에서 얻은 테이타를 x$^{2}$-분포에 따른 추정신뢰구간을 구해, 이를 토대로한 비대칭 삼각퍼지함수의 결정법을 제시하고, 이를 이용해서 구축한 시스템의 실행결과의 타당성을 비교하고자 한다.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.151-165
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• 2009

Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of its outstanding accuracy of control and forecasting area. We design a new classification model based on ANFIS and evaluate it in terms of classification accuracy. We identified ANFIS-based classification model has higher classification accuracy compared to existing classification model, C5.0 decision tree model by comparing their experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.506-511
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• 2006

This paper considers a method to design sliding mode observers for a class of uncertain systems using Linear Matrix Inequalities(LMI). In an LMI-based sliding mode observer design method for a class of uncertain systems the switching surface is set to be the difference between the observer and system output. In terms of LMIs, a necessary and sufficient condition is derived for the existence of a sliding-mode observer guaranteeing a stable sliding motion on the switching surface. The gain matrices of the sliding-mode observer are characterized using the solution of the LMI existence condition. The results are illustrated by an example.