• Title/Summary/Keyword: Fuzzy Membership function

Search Result 687, Processing Time 0.035 seconds

A fuzzy optimum design of axisymmetrically loaded thin shells of revolution

  • Kang, Moon-Myung;Mu, Zai-Gen;Kim, Seung-Deog;Kwun, Taek-Jin
    • Structural Engineering and Mechanics
    • /
    • v.7 no.3
    • /
    • pp.277-288
    • /
    • 1999
  • This paper presents a fuzzy optimum design of axisymmetrically loaded thin shells of revolution. This paper consists of two parts, namely: an elastic analysis using the new curved element for finite element analysis developed in this study for axisymmetrically loaded thin shells of revolution, and the volume optimization on the basis of results evaluated from the elastic analysis. The curved element to meridian direction is used to develop the computer program. The results obtained from the computer program are compared by exact solution of each analytic example. The fuzzy optimizations of thin shells of revolution are done using [Model 2] which is in the form of a conventional crisp objective function and constraints with non-membership function, and nonlinear optimum GINO (General Interactive Optimizer) programming. In this paper, design examples show that the fuzzy optimum designs of the steel water tank and the steel dome roof could provide significant cost savings.

On the Fuzzy Membership Function of Fuzzy Support Vector Machines for Pattern Classification of Time Series Data (퍼지서포트벡터기계의 시계열자료 패턴분류를 위한 퍼지소속 함수에 관한 연구)

  • Lee, Soo-Yong
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.17 no.6
    • /
    • pp.799-803
    • /
    • 2007
  • In this paper, we propose a new fuzzy membership function for FSVM(Fuzzy Support Vector Machines). We apply a fuzzy membership to each input point of SVM and reformulate SVM into fuzzy SVM (FSVM) such that different input points can make different contributions to the learning of decision surface. The proposed method enhances the SVM in reducing the effect of outliers and noises in data points. This paper compares classification and estimated performance of SVM, FSVM(1), and FSVM(2) model that are getting into the spotlight in time series prediction.

Linear Servo System by Fuzzy Control using Parameter Tuning of Membership Function (소속함수 파라미터 동조 퍼지제어에 의한 선형 서보 시스템)

  • 엄기환;손동설;이용구
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
    • /
    • v.9 no.3
    • /
    • pp.97-103
    • /
    • 1995
  • In this paper, for fuzzy control of linear servo system using the moving coil type linear DC motor, we propose a new fuzzy control method using parameter tuning for membership functions. A proposed fuzzy control method tunes parameters of membership function to have an appropriate control input signal for system when error exceeds predefined value and makes an inference using conventional fuzzy control rules when error reduces to a predefined value. To verify usefulness of a proposed fuzzy control method, making simulation and experiment, we compare with characteristics for conventional fuzzy control method.

  • PDF

퍼지 학습 규칙을 이용한 퍼지 신경회로망

  • 김용수
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 1997.11a
    • /
    • pp.180-184
    • /
    • 1997
  • This paper presents the fuzzy neural network which utilizes a fuzzified Kohonen learning uses a fuzzy membership value, a function of the iteration, and a intra-membership value instead of a learning rate. The IRIS data set if used to test the fuzzy neural network. The test result shows the performance of the fuzzy neural network depends on k and the vigilance parameter T.

  • PDF

Interval type-2 fuzzy radial basis function neural network (Interval 제 2 종 퍼지 radial basis function neural network)

  • Choe, Byeong-In;Lee, Jeong-Hun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 2006.11a
    • /
    • pp.19-22
    • /
    • 2006
  • Type-2 fuzzy 이론은 기존의 퍼지 이론보다 패턴의 불확실성에 대한 제어를 더 향상시킬 수 있다. 반면에 계산 량이 커지는 문제점 때문에 본 논문에서는 type-2 fuzzy set 대신에 secondary membership이 interval의 형태를 갖는 interval type-2 fuzzy set을 기존의 radial basis function(RBF) neural network에 적용시킨 interval type-2 fuzzy RBF neural network를 제안한다. 제안한 알고리즘은 interval type-2 fuzzy membership function에 의하여 패턴들의 불확실성을 좀 더 잘 제어하여 기존의 RBF neural network의 성능을 향상시킬 수 있다. 본 논문에서는 제안한 알고리즘의 타당성을 보이기 위하여 여러 데이터 집합에 대한 분류 결과를 보인다.

  • PDF

Analysis on Port Image for Development of Port-City Considered Environment using Fuzzy Theory (친환경 항만도시 개발을 위한 항만의 인식 분석 - 인천항만을 중심으로 -)

  • Jang Woon-Jae;Keum Jong-Soo
    • Proceedings of KOSOMES biannual meeting
    • /
    • 2005.11a
    • /
    • pp.79-84
    • /
    • 2005
  • This paper proposes an analysis to image of inchon port using fuzzy theory. After analysis, positive opinion is mean membership function 0.73 and positive membership function 0.27, negative opinion is mean membership function 0.69, negative membership function 0.31 about inchon port development. therefore, for port development need to accomodation of each opinion postive opinion is maximum decrease from 20 age to 30 age. and negative opinion is maximum increase from 10 age to 20 age. According to the results, port development need to high positive image and low negative image.

  • PDF

Development of Global Function Approximations of Desgin optimization Using Evolutionary Fuzzy Modeling

  • Kim, Seungjin;Lee, Jongsoo
    • Journal of Mechanical Science and Technology
    • /
    • v.14 no.11
    • /
    • pp.1206-1215
    • /
    • 2000
  • This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.

  • PDF

A construction of fuzzy controller using learning (학습을 이용한 퍼지 제어기의 구성)

  • 안상철;권욱현
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1992.10a
    • /
    • pp.484-489
    • /
    • 1992
  • The inference of fuzzy controller can be considered a mapping from the controller input to membership value. The membership value, a kind of weight, has a role to decide if the input is appropriate to the rule. The membership function is described by several values, which are decided by a learning method. The learning method is adopted from adaptive filtering theory. The simulation shows the proposed fuzzy controller can learn linear and nonlinear functions. the structure of the proposed fuzzy controller becomes a kind of neural network.

  • PDF

Multi-Attribute Decision-Making Method Applying a Novel Correlation Coefficient of Interval-Valued Neutrosophic Hesitant Fuzzy Sets

  • Liu, Chunfang
    • Journal of Information Processing Systems
    • /
    • v.14 no.5
    • /
    • pp.1215-1224
    • /
    • 2018
  • Interval-valued neutrosophic hesitant fuzzy set (IVNHFS) is an extension of neutrosophic set (NS) and hesitant fuzzy set (HFS), each element of which has truth membership hesitant function, indeterminacy membership hesitant function and falsity membership hesitant function and the values of these functions lie in several possible closed intervals in the real unit interval [0,1]. In contrast with NS and HFS, IVNHFS can be more flexibly used to deal with uncertain, incomplete, indeterminate, inconsistent and hesitant information. In this study, I propose the novel correlation coefficient of IVNHFSs and my paper discusses its properties. Then, based on the novel correlation coefficient, I develop an approach to deal with multi-attribute decision-making problems within the framework of IVNHFS. In the end, a practical example is used to show that the approach is reasonable and effective in dealing with decision-making problems.

Optimal Design Method of Quantization of Membership Function and Rule Base of Fuzzy Logic Controller using the Genetic Algorithm (유전자 알고리즘을 이용한 퍼지논리 제어기 소속함수의 양자화와 제어규칙의 최적 설계방식)

  • Chung Sung-Boo
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.9 no.3
    • /
    • pp.676-683
    • /
    • 2005
  • In this paper, we proposed a method that optimal values of fuzzy control rule base and quantization of membership function are searched by genetic algorithm. Proposed method searched the optimal values of membership function and control rules using genetic algorithm by off-line. Then fuzzy controller operates using these values by on-line. Proposed fuzzy control system is optimized the control rule base and membership function by genetic algorithm without expert's knowledge. We investigated proposed method through simulation and experiment using DC motor and one link manipulator, and confirmed the following usefulness.