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CFCM 기반 적응 뉴로-퍼지 시스템에 의한 비선형 시스템 모델링
곽근창,김성수,유정웅,전명근 충북대학교 컴퓨터정보통신 연구소 2002 컴퓨터정보통신연구 Vol.10 No.2
본 논문에서는 여러 분야에서 널리 응용되고 있는 적응 뉴로-퍼지 시스템에서의 효과적인 퍼지 규칙 생성 방법을 제안한다. 기존의 입력공간 그리드 분할을 이용한 ANFIS의 규칙 생성에 있어서는 얻어진 규칙의 수가 지수적으로 증가하는 단점이 있다. 이에, 본 연구에서는 조건부적인 퍼지 클러스터링(CFCM)을 이용하여 입·출력 데이터의 특성을 잘 반영할 수 있는 클러스터를 구하고, 퍼지 균등화 방법을 적용하여 출력변수의 소속함수를 자동 생성하도록 하였다. 이렇게 함으로써 적은 규칙 수를 갖으면서도 효율적인 퍼지 규칙을 얻을 수 있도록 하였다. 이들 방법의 유용함을 보이고자 정수장 응집제주입결정 모델링에 적용하여 제안된 방법이 기존의 연구보다 좋은 결과를 보임을 알 수 있었다. In this paper, an efficient fuzzy rule generation scheme for Adaptive Network-based Fuzzy Inference System(ANFIS) using the conditional fuzzy c-means(CFCM) and fuzzy equalization(FE) methods is proposed. Usually, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional ANFIS approaches. Therefore, GFCM clustering method is adopted to render the clusters which represent the given input and output fuzzy data and FE method is used to automatically construct the fuzzy membership functions. From this, one can systematically obtain a small size of fuzzy rules which shows satisfying performance for the given problems. Finally, we applied the proposed method to the nonlinear system modeling problems and obtained a better performance than conventional works.
레이저를 이용한 얇은 원통형 튜브에서의 유도초음파 발생
김현묵,정경일,장경영,안형근,곽노권,이창목 한국비파괴검사학회 2003 학술대회 논문집 Vol.- No.1
레이저를 이용하여 두께가 얇은 원통형 튜브에 유도초음파를 발생하고, 공기-결합 트랜스듀서를 이용하여 비접촉식으로 수신하였다. 원통형 튜브는 발전설비의 열교환기 및 증기발생기 튜브에 사용되는 인코넬(Inconnel)튜브이다. 레이저의 조사 경로에 선 배열 슬릿을 삽입하여 유도 초음파의 파장을 조절하였다. 파장이 일정할 때 발생되는 모드를 이론적인 분산선도로부터 예측하고, 예측된 위상속도로부터 공기-결합 트랜스듀서의 경사각을 결정하였다. 원통형 튜브에는 축대칭 모드가 지배적으로 발생하는 것으로 나타났다. 이러한 결과는 웨이블렛 변환에 의한 시간-주파수 선도와 이론적인 분산선도를 비교한 결과 증명되었다.
An Incremental Adaptive Neuro-Fuzzy Networks
Keun-Chang Kwak 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In this paper, we propose a method for constructing an Incremental Adaptive Neuro-Fuzzy Network (IANFN). In contrast to typical rule-based systems, the underlying principle is to consider a two-step development of Adaptive Neuro-Fuzzy Network (ANFN). First, we build a standard Linear Regression (LR) model which could be treated as a preliminary design capturing the linear part of the data. Next, all modeling discrepancies are compensated by a collection of rules that become attached to the regions of the input space in which the error becomes localized. The incremental network is constructed by building a collection of information granules through some specialized fuzzy clustering, called Context-based Fuzzy C-Means (CFCM) that is guided by the distribution of error of the linear part of its development. The experimental results reveal that the proposed incremental network shows a good approximation and generalization capability in comparison with the general method.
Genetically Optimized Linguistic Models
Keun-Chang Kwak 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
This paper is concerned with Linguistic Models (LM) optimized by Genetic Algorithm (GA) and developed their comprehensive framework. The main characteristics of LM are user-centric and inherently dwell upon collections of highly interpretable and user-oriented entities such as information granules. The objective of this paper is to present an organization of overall optimization process and come up with a specification of several evaluation mechanisms of the performance of the models. The underlying design tool guiding the development of LM revolves around the augmented version of fuzzy clustering known a context-based fuzzy c-means. The optimization design based on GA determines the number of cluster generated by each linguistic context and fuzzification factor related to information granules in the input and output process. The experimental study comes with coagulant dosing process in a water purification plant. Furthermore we contrast the performance of genetically opimized LM in comparison with other radial basis function networks and LM itself.
Face Recognition Using an Enhanced Independent Component Analysis Approach
Kwak, Keun-Chang,Pedrycz, Witold Institute of Electrical and Electronics Engineers 2007 IEEE transactions on neural networks Vol.18 No.2
<P>This paper is concerned with an enhanced independent component analysis (ICA) and its application to face recognition. Typically, face representations obtained by ICA involve unsupervised learning and high-order statistics. In this paper, we develop an enhancement of the generic ICA by augmenting this method by the Fisher linear discriminant analysis (LDA); hence, its abbreviation, FICA. The FICA is systematically developed and presented along with its underlying architecture. A comparative analysis explores four distance metrics, as well as classification with support vector machines (SVMs). We demonstrate that the FICA approach leads to the formation of well-separated classes in low-dimension subspace and is endowed with a great deal of insensitivity to large variation in illumination and facial expression. The comprehensive experiments are completed for the facial-recognition technology (FERET) face database; a comparative analysis demonstrates that FICA comes with improved classification rates when compared with some other conventional approaches such as eigenface, fisherface, and the ICA itself</P>