• Title/Summary/Keyword: fuzzy membership function distribution

Search Result 33, Processing Time 0.027 seconds

An Analysis of Fuzzy Survey Data Based on the Maximum Entropy Principle (최대 엔트로피 분포를 이용한 퍼지 관측데이터의 분석법에 관한 연구)

  • 유재휘;유동일
    • Journal of the Korea Society of Computer and Information
    • /
    • v.3 no.2
    • /
    • pp.131-138
    • /
    • 1998
  • In usual statistical data analysis, we describe statistical data by exact values. However, in modem complex and large-scale systems, it is difficult to treat the systems using only exact data. In this paper, we define these data as fuzzy data(ie. Linguistic variable applied to make the member-ship function.) and Propose a new method to get an analysis of fuzzy survey data based on the maximum entropy Principle. Also, we propose a new method of discrimination by measuring distance between a distribution of the stable state and estimated distribution of the present state using the Kullback - Leibler information. Furthermore, we investigate the validity of our method by computer simulations under realistic situations.

  • PDF

Fuzzy histogram in estimating loss distributions for operational risk (운영 위험 관련 손실 분포 - 퍼지 히스토그램의 효과)

  • Pak, Ro-Jin
    • Journal of the Korean Data and Information Science Society
    • /
    • v.20 no.4
    • /
    • pp.705-712
    • /
    • 2009
  • Histogram is the oldest and most widely used density estimator for presentation and exploration of observed univariate data. The structure of a histogram really depends on the number of bins and the width of the bins, so that slight changes on bins can produce totally different shape of a histogram. In order to solve this problem the fuzzy histogram was introduced and the result was good enough (Loquin and Strauss, 2008). In particular, when estimating loss distribution related with operational risk a histogram has been widely used. In this article, instead of an ordinary histogram we try to use a fuzzy histogram for estimating loss distribution and show that a fuzzy histogram provide more stable results.

  • PDF

The Study of Criteria Weight for Taiwan National Quality Award by Fuzzy Hierarchical Analysis

  • Li, Shao-Chang;Fu, Hsin-Pin
    • International Journal of Quality Innovation
    • /
    • v.7 no.2
    • /
    • pp.83-96
    • /
    • 2006
  • In this paper, fuzzy hierarchical analysis (FHA) is used to explore the process by which the criteria weights of the Taiwan National Quality Award (TNQA) are assigned by TNQA committee members. Each member is allowed to employ fuzzy scales in place of exact scales. Each pairwise comparison of criteria is made through a questionnaire from each TNQA committee member. The membership function of trapezoidal fuzzy numbers is introduced to specify TNQA committee members' intentions. After FHA, the reasonable range of each criterion weight of TNQA is determined. The current criteria weights of TNQA are properly verified.

Optimal Design of Fuzzy-Neural Networkd Structure Using HCM and Hybrid Identification Algorithm (HCM과 하이브리드 동정 알고리즘을 이용한 퍼지-뉴럴 네트워크 구조의 최적 설계)

  • Oh, Sung-Kwun;Park, Ho-Sung;Kim, Hyun-Ki
    • The Transactions of the Korean Institute of Electrical Engineers D
    • /
    • v.50 no.7
    • /
    • pp.339-349
    • /
    • 2001
  • This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

  • PDF

A novel evidence theory model and combination rule for reliability estimation of structures

  • Tao, Y.R.;Wang, Q.;Cao, L.;Duan, S.Y.;Huang, Z.H.H.;Cheng, G.Q.
    • Structural Engineering and Mechanics
    • /
    • v.62 no.4
    • /
    • pp.507-517
    • /
    • 2017
  • Due to the discontinuous nature of uncertainty quantification in conventional evidence theory(ET), the computational cost of reliability analysis based on ET model is very high. A novel ET model based on fuzzy distribution and the corresponding combination rule to synthesize the judgments of experts are put forward in this paper. The intersection and union of membership functions are defined as belief and plausible membership function respectively, and the Murfhy's average combination rule is adopted to combine the basic probability assignment for focal elements. Then the combined membership functions are transformed to the equivalent probability density function by a normalizing factor. Finally, a reliability analysis procedure for structures with the mixture of epistemic and aleatory uncertainties is presented, in which the equivalent normalization method is adopted to solve the upper and lower bound of reliability. The effectiveness of the procedure is demonstrated by a numerical example and an engineering example. The results also show that the reliability interval calculated by the suggested method is almost identical to that solved by conventional method. Moreover, the results indicate that the computational cost of the suggested procedure is much less than that of conventional method. The suggested ET model provides a new way to flexibly represent epistemic uncertainty, and provides an efficiency method to estimate the reliability of structures with the mixture of epistemic and aleatory uncertainties.

Improvement of the PFCM(Possibilistic Fuzzy C-Means) Clustering Method (PFCM 클러스터링 기법의 개선)

  • Heo, Gyeong-Yong;Choe, Se-Woon;Woo, Young-Woon
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.13 no.1
    • /
    • pp.177-185
    • /
    • 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.

Enhanced Binarization Method using Fuzzy Membership Function (퍼지 소속 함수를 애용한 개선된 이진화 방법)

  • Kim Kwang Baek;Kim Young Ju
    • Journal of the Korea Society of Computer and Information
    • /
    • v.10 no.1 s.33
    • /
    • pp.67-72
    • /
    • 2005
  • Most of image binarization algorithms analyzes the intensity distribution using the histogram for the determination of threshold value. When the intensity difference between the foreground object and the background is great, the histogram shows the tendency to be bimodal and the selection of the histogram valley as the threshold value shows the good result. On the other side. when the intensity difference is not great and the histogram doesn't show the bimodal property, the histogram analysis doesn't support the selection of the proper threshold value. This Paper Proposed the novel binarization method that applies the fuzzy membership function to each color value on the RGB color model and, by using the operation results, separates the features having the great readability from the background. The proposed method prevents the loss of information incurred by the gray scale conversion by using the RGB color model and extracts effectively the readable features by using the fuzzy inference Compared with the traditional binarization methods, the proposed method is able to remove the majority of noise areas and show the improved results on the image of transport containers , etc.

  • PDF

The Fuzzy Traffic Control Method for ABR Service (ABR 서비스에서 퍼지 트래픽 제어 방식)

  • Yu, Jae-Taek;Kim, Yong-U;Lee, Jin-Lee;Lee, Gwang-Hyeong
    • The Transactions of the Korea Information Processing Society
    • /
    • v.3 no.7
    • /
    • pp.1880-1893
    • /
    • 1996
  • In this paper, we propose the fuzzy traffic control method in ABR service for the effective use of ATM link. This method, a modified version of EPRCA which is one of rate control methods in ABR service, controls the values of the transmission rates of source by using the fuzzy traffic inference based on switch buffer size and buffer variate rate. For this method, we developed a model and algorithm of the fuzzy traffic control method and a fuzzy traffic controller, after studying fuzzy and neural networks which applied to ATM traffic control and EPRCA. For the fuzzy traffic controller, we also designed a membership function, fuzzy control rules and a max-min inferencing method. We conducted a simulation and compared the link utilization of the fuzzy traffic control method with that of the EPRCA method. The results of the simulation indicated that, compared to EPRCA, the fuzzy traffic control method improves the link utilization by 2.3% in a normal distribution model and by 2.7% in the MMPP model of the source.

  • PDF

Strategic Pricing Framework for Closed Loop Supply Chain with Remanufacturing Process using Nonlinear Fuzzy Function (재 제조 프로세스를 가진 순환 형 SCM에서의 비선형 퍼지 함수 기반 가격 정책 프레임웍)

  • Kim, Jinbae;Kim, Taesung;Lee, Hyunsoo
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.40 no.4
    • /
    • pp.29-37
    • /
    • 2017
  • This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises' sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.

Fuzzy One Class Support Vector Machine (퍼지 원 클래스 서포트 벡터 머신)

  • Kim, Ki-Joo;Choi, Young-Sik
    • Journal of Internet Computing and Services
    • /
    • v.6 no.3
    • /
    • pp.159-170
    • /
    • 2005
  • OC-SVM(One Class Support Vector Machine) avoids solving a full density estimation problem, and instead focuses on a simpler task, estimating quantiles of a data distribution, i.e. its support. OC-SVM seeks to estimate regions where most of data resides and represents the regions as a function of the support vectors, Although OC-SVM is powerful method for data description, it is difficult to incorporate human subjective importance into its estimation process, In order to integrate the importance of each point into the OC-SVM process, we propose a fuzzy version of OC-SVM. In FOC-SVM (Fuzzy One-Class Support Vector Machine), we do not equally treat data points and instead weight data points according to the importance measure of the corresponding objects. That is, we scale the kernel feature vector according to the importance measure of the object so that a kernel feature vector of a less important object should contribute less to the detection process of OC-SVM. We demonstrate the performance of our algorithm on several synthesized data sets, Experimental results showed the promising results.

  • PDF