• Title/Summary/Keyword: Fuzzy C means clustering

Search Result 142, Processing Time 0.116 seconds

Identification of Plastic Wastes by Using Fuzzy Radial Basis Function Neural Networks Classifier with Conditional Fuzzy C-Means Clustering

  • Roh, Seok-Beom;Oh, Sung-Kwun
    • Journal of Electrical Engineering and Technology
    • /
    • v.11 no.6
    • /
    • pp.1872-1879
    • /
    • 2016
  • The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

Design of Radial Basis Function with the Aid of Fuzzy KNN and Conditional FCM (퍼지 kNN과 Conditional FCM을 이용한 퍼지 RBF의 설계)

  • Roh, Seok-Beon;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.58 no.6
    • /
    • pp.1223-1229
    • /
    • 2009
  • The performance of Radial Basis Function Neural Networks depends on setting up the Radial Basis Functions over the input space which are the important design procedure of Radial Basis Function Neural Networks. The existing method to initialize the location of the radial basis functions over the input space is to use the conditional fuzzy C-means clustering. However, the researchers which are interested in the conditional fuzzy C-means clustering cannot get as good modeling performance as they expect because the conditional fuzzy C-means clustering cannot project the information which is extracted over the output space into the input space. To compensate the above mentioned drawback of the conditional fuzzy C-means clustering, we apply a fuzzy K-nearest neighbors approach to project the auxiliary information defined over the output space into the input space without lose of the information.

Genetically Optimization of Fuzzy C-Means Clustering based Fuzzy Neural Networks (Subtractive Clustering 알고리즘을 이용한 퍼지 RBF 뉴럴네트워크의 동정)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
    • /
    • /
    • pp.239-240
    • /
    • 2008
  • 본 논문에서는 Subtractive clustering 알고리즘을 이용한 Fuzzy Radial Basis Function Neural Network (FRBFNN)의 규칙 수를 자동적으로 생성하는 방법을 제시한다. FRBFNN은 멤버쉽 함수로써 기존 RBFNN에서 가우시안이나 타원형 형태의 특정 RBF를 사용하는 구조와 달리 Fuzzy C-Means clustering 알고리즘에서 사용하는 거리에 기한 멤버쉽 함수를 사용하여 전반부의 공간 분할 및 활성화 레벨을 결정하는 구조이다. 본 논문에서는 데이터의 밀집도에 기반을 두어 클러스터링을 하는 Subtractive clustering 알고리즘을 사용하여 퍼지 규칙의 수와 같은 의미를 갖는 분할할 입력공간의 수와 분할된 입력공간의 중심값을 동정하며, Least Square Estimator (LSE) 알고리즘을 사용하여 후반부 다항식의 계수를 추정 한다.

  • PDF

A Study on the Fault Current Discrimination Using Enhanced Fuzzy C-Means Clustering (개선된 퍼지 C-Means 클러스터링을 이용한 고장전류판별에 관한 연구)

  • Jeong, Jong-Won;Lee, Joon-Tark
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.57 no.11
    • /
    • pp.2102-2107
    • /
    • 2008
  • This paper demonstrates a enhanced FCM to identify the causes of ground faults in power distribution systems. The discrimination scheme which can automatically recognize the fault causes is proposed using Fuzzy RBF networks. By using the actual fault data, it is shown that the proposed method provides satisfactory results for identifying the fault causes.

A Design of Fuzzy Classifier with Hierarchical Structure (계층적 구조를 가진 퍼지 패턴 분류기 설계)

  • Ahn, Tae-Chon;Roh, Seok-Beom;Kim, Yong Soo
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.24 no.4
    • /
    • pp.355-359
    • /
    • 2014
  • In this paper, we proposed the new fuzzy pattern classifier which combines several fuzzy models with simple consequent parts hierarchically. The basic component of the proposed fuzzy pattern classifier with hierarchical structure is a fuzzy model with simple consequent part so that the complexity of the proposed fuzzy pattern classifier is not high. In order to analyze and divide the input space, we use Fuzzy C-Means clustering algorithm. In addition, we exploit Conditional Fuzzy C-Means clustering algorithm to analyze the sub space which is divided by Fuzzy C-Means clustering algorithm. At each clustered region, we apply a fuzzy model with simple consequent part and build the fuzzy pattern classifier with hierarchical structure. Because of the hierarchical structure of the proposed pattern classifier, the data distribution of the input space can be analyzed in the macroscopic point of view and the microscopic point of view. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

Segmentation of Color Image by Subtractive and Gravity Fuzzy C-means Clustering (차감 및 중력 fuzzy C-means 클러스터링을 이용한 칼라 영상 분할에 관한 연구)

  • Jin, Young-Goun;Kim, Tae-Gyun
    • Journal of IKEEE
    • /
    • v.1 no.1
    • /
    • pp.93-100
    • /
    • 1997
  • In general, fuzzy C-means clustering method was used on the segmentation of true color image. However, this method requires number of clusters as an input. In this study, we suggest new method that uses subtractive and gravity fuzzy C-means clustering. We get number of clusters and initial cluster centers by applying subtractive clustering on color image. After coarse segmentation of the image, we apply gravity fuzzy C-means for optimizing segmentation of the image. We show efficiency of the proposed algorithm by qualitative evaluation.

  • PDF

The Quantization of Lumbar Ultrasonographic Images using Fuzzy C-Means Clustering (퍼지 C-Means 클러스터링을 이용한 요부 초음파 영상의 양자화)

  • Hong, Dong-Jin;Kim, Kwang-Baek
    • Proceedings of the Korean Society of Computer Information Conference
    • /
    • /
    • pp.301-302
    • /
    • 2013
  • 본 논문에서는 초음파 영상에서 퍼지 C-Means 클러스터링을 이용한 양자화 기법을 제안한다. 제안된 방법은 초음파 영상에서 나타난 명암도를 이용하여 n개의 그룹으로 클러스터링한다. 그리고 각 클러스터의 중심 값을 기준으로 정렬한 뒤, 각 그룹에 지정된 색상을 요부 초음파 영상에서 나타낸다. 본 논문에서 제안하는 기법을 적용한 요부 초음파 영상과 일반적으로 자주 이용되는 히스토그램 기반 양자화 기법을 적용한 요부 초음파 영상을 비교하였을 때, 본 논문에서 제안하는 퍼지 C-Means 클러스터링을 이용한 양자화를 적용한 영상이 근육 내의 지방을 분석하는데 효과적인 것을 확인할 수 있었다.

  • PDF

Switching Regression Analysis via Fuzzy LS-SVM

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
    • /
    • v.17 no.2
    • /
    • pp.609-617
    • /
    • 2006
  • A new fuzzy c-regression algorithm for switching regression analysis is presented, which combines fuzzy c-means clustering and least squares support vector machine. This algorithm can detect outliers in switching regression models while yielding the simultaneous estimates of the associated parameters together with a fuzzy c-partitions of data. It can be employed for the model-free nonlinear regression which does not assume the underlying form of the regression function. We illustrate the new approach with some numerical examples that show how it can be used to fit switching regression models to almost all types of mixed data.

  • PDF

Improved Classification Algorithm using Extended Fuzzy Clustering and Maximum Likelihood Method

  • Jeon Young-Joon;Kim Jin-Il
    • Proceedings of the IEEK Conference
    • /
    • /
    • pp.447-450
    • /
    • 2004
  • This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

  • PDF

Fuzzy Identification by means of Fuzzy Inference Method and Its Application to Wate Water Treatment System (퍼지추론 방법에 의한 퍼지동정과 하수처리공정시스템 응용)

  • 오성권;주영훈;남위석;우광방
    • Journal of the Korean Institute of Telematics and Electronics B
    • /
    • v.31B no.6
    • /
    • pp.43-52
    • /
    • 1994
  • A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of ``IF....,THEN...', using the theories of optimization theory , linguistic fuzzy implication rules and fuzzy c-means clustering. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 2), and modified linear inference (type 3). In order to identify premise structure and parameter of fuzzy implication rules, fuzzy c- means clustering and modified complex method are used respectively and the least sequare method is utilized for the identification of optimum consequence parameters. Time series data for gas furance and those for sewage treatment process are used to evaluate the performance of the proposed rule-based fuzzy modeling. Comparison shows that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

  • PDF