• Title, Summary, Keyword: fuzzy model

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A Fuzzy Traffic Controller Considering the spillback on the Multiple Crossroads

  • Kim, Young-Sik
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.6
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    • pp.722-728
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    • 2003
  • In this paper, we propose a fuzzy traffic controller of Sugeno`s fuzzy model so as to model the nonlinear characteristics of controlling the traffic light. It use a degree of the traffic congestion of the preceding roads as an input so that it can cope with traffic congestion appropriately, which causes the loss of fuel and our discomfort. First, in order to construct fuzzy traffic controller of Sugeno`s fuzzy model, we model the control process of the traffic light by using Mamdani`s fuzzy model, which has the uniform membership functions of the same size and shape. Second, we make Mamdani`s fuzzy model with the non-uniform membership functions so that it can exactly reflect the knowledge of experts and operators. Last, we construct the fuzzy traffic controller of Sugeno`s fuzzy model by learning from the input/output data, which is retrieved from Mamdani`s fuzzy model with the non-uniform membership functions. We compared and analyzed the fixed traffic light controller, the fuzzy traffic controller of Mamdani`s fuzzy model and the fuzzy traffic controller of Sugeno`s fuzzy model by using the delay time and the proportion of the entered vehicles to the occurred vehicles. As a result of comparison, the fuzzy traffic controller of Sugeno`s fuzzy model showed the best performance.

Optimization of Fuzzy Systems by Means of GA and Weighting Factor (유전자 알고리즘과 하중값을 이용한 퍼지 시스템의 최적화)

  • Park, Byoung-Jun;Oh, Sung-Kwun;Ahn, Tae-Chon;Kim, Hyun-Ki
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.6
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    • pp.789-799
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    • 1999
  • In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

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Separate Fuzzy Regression with Fuzzy Input and Output

  • Choi, Seung-Hoe
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.183-193
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    • 2007
  • This paper shows that a response function for the center of fuzzy output nay not be the same as that for the spread in a fuzzy linear regression model and then suggests a separate fuzzy regression model makes a distinction between response functions of the center and the spread of fuzzy output. Also we use a least squares method to estimate the separate fuzzy regression model and compare an accuracy of proposed model with another fuzzy regression model developed by Diamond (1988) and Kao and Chyu (2003).

Level control of single water tank systems using Fuzzy-PID technique

  • Lee, Yun-Hyung;Jin, Gang-Gyoo;So, Myung-Ok
    • Journal of Advanced Marine Engineering and Technology
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    • v.38 no.5
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    • pp.550-556
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    • 2014
  • In this study, for the control of a single water tank system, a fuzzy-PID controller design technique based on a fuzzy model is investigated. For this purpose, a water tank system is linearized as a number of submodels depending on the operating point, and a fuzzy model is obtained by fuzzy combining. Each submodel is approximated as a first order time delay model, and a PID controller is designed using several existing tuning techniques. Then, through the fuzzy combination of this controller using the same method as that of the fuzzy model, a fuzzy-PID controller is designed. For the proposed technique, a simulation is performed using the fuzzy model of a water tank system, and the validity is examined by comparing its performance with that of a PID controller.

FUZZY REGRESSION MODEL WITH MONOTONIC RESPONSE FUNCTION

  • Choi, Seung Hoe;Jung, Hye-Young;Lee, Woo-Joo;Yoon, Jin Hee
    • Communications of the Korean Mathematical Society
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    • v.33 no.3
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    • pp.973-983
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    • 2018
  • Fuzzy linear regression model has been widely studied with many successful applications but there have been only a few studies on the fuzzy regression model with monotonic response function as a generalization of the linear response function. In this paper, we propose the fuzzy regression model with the monotonic response function and the algorithm to construct the proposed model by using ${\alpha}-level$ set of fuzzy number and the resolution identity theorem. To estimate parameters of the proposed model, the least squares (LS) method and the least absolute deviation (LAD) method have been used in this paper. In addition, to evaluate the performance of the proposed model, two performance measures of goodness of fit are introduced. The numerical examples indicate that the fuzzy regression model with the monotonic response function is preferable to the fuzzy linear regression model when the fuzzy data represent the non-linear pattern.

A METHOD OF DEVELOPING SOFT SENSOR MODEL USING FUZZY NEURAL NETWORK

  • Chang, Yuqing;Wang, Fuli;Lin, Tian
    • Proceedings of the Korea Society for Simulation Conference
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    • pp.103-109
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    • 2001
  • Soft sensor is an effective method to deal with the estimation of variables, which are difficult to measure because of the reasons of economy or technology. Fuzzy logic system can be used to develop the soft sensor model by infinite rules, but the fuzzy dividing of variable sets is a key problem to achieve an accurate fuzzy logic model, In this paper, we proposed a new method to develop soft sensor model based on fuzzy neural network. First, using a novel method to divide the variable fuzzy sets by the process input and output data. Second, developing the fuzzy logic model based on that fuzzy set dividing. After that, expressing the fuzzy system with a fuzzy neural network and getting the initial soft sensor model based FNN. Last, adjusting the relative parameters of soft sensor model by the BP learning method. The effectiveness of the method proposed and the preferable generalization ability of soft sensor model built are demonstrated by the simulation.

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CENSORED FUZZY REGRESSION MODEL

  • Choi, Seung-Hoe;Kim, Kyung-Joong
    • Journal of the Korean Mathematical Society
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    • v.43 no.3
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    • pp.623-634
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    • 2006
  • Various methods have been studied to construct a fuzzy regression model in order to present a fuzzy relation between a dependent variable and an independent variable. However, in the fuzzy regression analysis the value of the center point of estimated fuzzy output may be either greater than the value of the right endpoint or smaller than the value of the left endpoint. In the case, we cannot predict the fuzzy output properly. This paper presents sufficient conditions to construct the fuzzy regression model using several methods investigated by some authors and then introduces the censored fuzzy regression model using the censored samples to manipulate the problem of crossing of the center and the end points of the estimated fuzzy number. Examples show that the censored fuzzy regression model is an extension of the fuzzy regression model and also it improves the problem of crossing.

Development and Analysis of Fuzzy Overall Equipment Effectiveness (OEE) in TPM (TPM에서 퍼지 OEE 모형의 개발 및 분석)

  • Choi, Sungwoon
    • Journal of the Korea Management Engineers Society
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    • v.23 no.4
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    • pp.87-103
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    • 2018
  • This paper introduces the method to develop two main types of the fuzzy OEE (Overall Equipment Effectiveness) models via triangular membership function for measuring uncertainty. The fuzzy OEE includes model type 1 and model type 2. The model type 1 is used when the theoretical machine speed only reflects the time loss whereas model type 2 is used when the actual machine speed reflects both time and speed loss. Model type 2 has shown to perform a lower availability rate and a higher performance rate compared to model type 1. In addition, the fuzzy UPH (Unit Per Hour) which is derived from using the fuzzy OEE is presented to satisfy demand uncertainty. The fuzzy UPH can easily measure the fuzzy tact time and cycle time by reciprocating itself. Finally, this study demonstrates the fuzzy OEE models using IVIFS (Interval-Valued Intuitionistic Fuzzy Set) based on the characterization via membership function, non-membership function and hesitant function. For the purpose of analyzing the fuzzy system OEE, the OEE for each machine of plant structure is considered triangular interval-valued intuitionistic fuzzy number. Regardless of plant structure, the validity degree of fuzzy membership function of system OEE decreases when the number of machine with worst value of the validity degree increases. Corresponding examples are presented in this paper for practitioner to understand the applicability and practicability of the proposed fuzzy OEE methods.

Color Data Clustering Algorithm using Fuzzy Color Model (퍼지컬러 모델을 이용한 컬러 데이터 클러스터링 알고리즘1)

  • Kim, Dae-Won;Lee, Kwang H.
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.119-122
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    • 2002
  • The research Interest of this paper is focused on the efficient clustering task for an arbitrary color data. In order to tackle this problem, we have tiled to model the inherent uncertainty and vagueness of color data using fuzzy color model. By laking a fuzzy approach to color modeling, we could make a soft decision for the vague regions between neighboring colors. The proposed fuzzy color model defined a three dimensional fuzzy color ball and color membership computation method with the two inter-color distance measures. With the fuzzy color model, we developed a new fuzzy clustering algorithm for an efficient partition of color data. Each fuzzy cluster set has a cluster prototype which is represented by fuzzy color centroid.

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Self-Organizing Fuzzy Modeling Based on Hyperplane-Shaped Clusters (다차원 평면 클러스터를 이용한 자기 구성 퍼지 모델링)

  • Koh, Taek-Beom
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.12
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    • pp.985-992
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    • 2001
  • This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

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