• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.118-121
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• 1996

This paper describes ECG signal labeling based on Fuzzy clustering, which is necessary at automated ECG diagnosis. The NPPA(Non parametric partitioning algorithm) compares the correlations of wave forms, which tends to recognize the same wave forms as different when the wave forms have a little morphological variation. We propose to apply Fuzzy clustering to ECG QRS Complex labeling, which prevents the errors to mistake by using If-then comparision. The process is divided into two parts. The first part is a parameters extraction process from ECG signal, which is composed of filtering, QRS detection by mapping to a phase space by time delay coordinates and generation of characteristic vectors. The second is fuzzy clustering by FCM(Fuzzy c-means), which is composed of a clustering, an assessment of cluster validity and labeling.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.177-185
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• 2009

Cluster analysis or clustering is a kind of unsupervised learning method in which a set of data points is divided into a given number of homogeneous groups. Fuzzy clustering method, one of the most popular clustering method, allows a point to belong to all the clusters with different degrees, so produces more intuitive and natural clusters than hard clustering method does. Even more some of fuzzy clustering variants have noise-immunity. In this paper, we improved the Possibilistic Fuzzy C-Means (PFCM), which generates a membership matrix as well as a typicality matrix, using Gath-Geva (GG) method. The proposed method has a focus on the boundaries of clusters, which is different from most of the other methods having a focus on the centers of clusters. The generated membership values are suitable for the classification-type applications. As the typicality values generated from the algorithm have a similar distribution with the values of density function of Gaussian distribution, it is useful for Gaussian-type density estimation. Even more GG method can handle the clusters having different numbers of data points, which the other well-known method by Gustafson and Kessel can not. All of these points are obvious in the experimental results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.170-173
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• 2002

본 논문에서는 여러 분야에서 널리 응용되고 있는 적응 뉴로-퍼지 시스템(ANFIS)에서의 효과적인 퍼지 규칙 생성방법을 제안한다. ANFIS의 성능 개선을 위해 구조동정을 수행함에 있어서 전제부 파라미터는 EM(Expectation-Maximization) 알고리즘을 적용하였으며, 파라미터학습은 Jang에 의한 하이브리드 방법을 적용한다. 여기서 초기의 중심과 분산을 구하기 위해 FCM(Fuzzy c-means) 클러스터링 기법을 사용하였다. 이렇게 함으로서 적은 규칙 수를 가지면서도 효율적인 퍼지 규칙을 얻을 수 있도록 하였다. 이들 방법의 유용함을 보이고자 Box-Jenkins의 가스로 데이터에 적용하여 제안된 방법이 이전의 연구보다 좋은 결과를 보임을 보이고자 한다

• Journal of IKEEE
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• v.15 no.3
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• pp.191-197
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• 2011

An image segmentation model using fuzzy c-means with intuition (FCM-I) model is proposed for the segmentation of magnetic resonance image in this paper. In FCM-I, a measurement called intuition level is adopted so that the intuition level helps to alleviate the effect of noises. A practical magnetic resonance image data set is used for image segmentation experiment and the performance is compared with those of some conventional algorithms. Results show that the segmentation method based on FCM-I compares favorably to several conventional clustering algorithms. Since FCM-I produces cluster prototypes less sensitive to noises and to the selection of involved parameters than the other algorithms, FCM-I is a good candidate for image segmentation problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.767-778
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• 2003

This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.587-594
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• 2005

This paper proposes a multi-level thresholding method for image segmentation using fuzzy clustering algorithm in transition region. Most of threshold-based image segmentation methods determine thresholds based on the histogram distribution of a given image. Therefore, the methods have difficulty in determining thresholds for real-image, which has a complex and undistinguished distribution, and demand much computational time and memory size. To solve these problems, we determine thresholds for real-image using fuzzy clustering algorithm after extracting transition region consisting of essential and important components in image. Transition region is extracted based on Inか entropy, which is robust to noise and is well-known as a tool that describes image information. And fuzzy clustering algorithm can determine optimal thresholds for real-image and be easily extended to multi-level thresholding. The experimental results demonstrate the effectiveness of the proposed method for performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.82-89
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• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.177-180
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• 2001

영상에 존재하는 객체들을 인식하기 위해서는 먼저 영상의 영역분할이 필요하다. 통계적 모델을 이용한 영상의 영역분할은 미리서 분할하고자 하는 클러스터의 수를 결정한 후 이를 토대로 영상을 분할하게 된다. 그러나 영상마다 특성상 분할하고자 하는 클러스터 수가 다를 경우 이를 수동적으로 해주는 것은 비능률적이다. 따라서 본 논문은 영상의 영역분할에 통계적 모델에서 미리 결정해줘야 하는 클러스터의 수 문제를 자동으로 검출하고 퍼지 c-Means 글러스터링 알고리즘을 통한 영상의 영역분할 시 노이즈문제를 이웃한 픽셀들의 멤버쉽 값을 평균화합으로써 해결하는 방법을 제안하였다.