• Title/Summary/Keyword: fuzzy modeling

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Fuzzy identification by means of fuzzy inference method (퍼지추론 방법에 의한 퍼지동정)

  • 안태천;황형수;오성권;김현기;우광방
    • 제어로봇시스템학회:학술대회논문집
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    • 1993.10a
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    • pp.200-205
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    • 1993
  • A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type 1), linear inference (type 2), and modified linear inference (type 3). The fuzzy c-means clustering and modified complex methods are used in order to identify the preise structure and parameter of fuzzy implication rules, respectively and the least square method is utilized for the identification of optimal consequence parameters. Time series data for gas funace and sewage treatment processes are used to evaluate the performances of the proposed rule-based fuzzy modeling.

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A combustion control modeling of coke oven by Swarm-based fuzzy system (스왐기반 퍼지시스템을 이용한 코크오븐 연소제어 모델링)

  • Ko, Ean-Tae;Hwang, Seok-Kyun;Lee, Jin-S.
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.493-495
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    • 2005
  • This paper proposes a swarm-based fuzzy system modeling technique for coke oven combustion control diagnosis. The coke plant produces coke for the blast furnace plant in steel making process by charging coal into oven and supplying gas to carbonize it. A conventional mathematical model for coke oven combustion control has been used to control the amount of gas input, but it does not work well because of highly nonlinear feature of coke plant. To solve this problem, swarm-based fuzzy system modeling technique is suggested to construct a diagnosis model of coke oven combustion control. Based on the measured input-output data pairs, the fuzzy rules are generated and the parameters are tuned by the PSO(Particle Swarm Optimizer) to increase the accuracy of the fuzzy system is operated. This system computes the proper amount of gas input taking the operation conditions of coke oven into account, and compares the computed result with the supplied gas input.

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T-S Fuzzy Modeling of Synchronous Generator in a Power System (전력계통 동기발전기의 T-S Fuzzy 모델링)

  • Lee, Hee-Jin;Baek, Seung-Mook;Park, Jung-Wook
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.9
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    • pp.1642-1651
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    • 2008
  • The dynamic behavior of power systems is affected by the interactions between linear and nonlinear components. To analyze those complicated power systems, the linear approaches have been widely used so far. Especially, a synchronous generator has been designed by using linear models and traditional techniques. However, due to its wide operating range, complex dynamics, transient performances, and its nonlinearities, it cannot be accurately modeled as linear methods based on small-signal analysis. This paper describes an application of the Takaki-Sugeno (T-S) fuzzy method to model the synchronous generator in a single-machine infinite bus (SMIB) system. The T-S fuzzy model can provide a highly nonlinear functional relation with a comparatively small number of fuzzy rules. The simulation results show that the proposed T-S fuzzy modeling captures all dynamic characteristics for the synchronous generator, which are exactly same as those by the conventional modeling method.

Modeling of Dynamic Hysteresis Based on Takagi-Sugeno Fuzzy Duhem Model

  • Lee, Sang-Yun;Park, Mignon;Baek, Jaeho
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.13 no.4
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    • pp.277-283
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    • 2013
  • In this study, we propose a novel method for modeling dynamic hysteresis. Hysteresis is a widespread phenomenon that is observed in many physical systems. Many different models have been developed for representing a hysteretic system. Among them, the Duhem model is a classical nonlinear dynamic hysteresis model satisfying the properties of hysteresis. The purpose of this work is to develop a novel method that expresses the local dynamics of the Duhem model by a linear system model. Our approach utilizes a certain type of fuzzy system that is based on Takagi-Sugeno (T-S) fuzzy models. The proposed T-S fuzzy Duhem model is achieved by fuzzy blending of the linear system model. A simulated example applied to shape memory alloy actuators, which have typical hysteretic properties, illustrates the applicability of our proposed scheme.

Fused Fuzzy Logic System for Corrupted Time Series Data Analysis (훼손된 시계열 데이터 분석을 위한 퍼지 시스템 융합 연구)

  • Kim, Dong Won
    • Journal of Internet of Things and Convergence
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    • v.4 no.1
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    • pp.1-5
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    • 2018
  • This paper is concerned with the modeling and identification of time series data corrupted by noise. As modeling techniques, nonsingleton fuzzy logic system (NFLS) is employed for the modeling of corrupted time series. Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data or the input data to the fuzzy logic system are corrupted by noise. Simulation results of the Mackey-Glass time series data will be demonstrated to show the performance of the modeling methods. As a result, NFLS does a much better job of modeling noisy time series data than does a traditional Mamdani FLS.

Wavelet-Based Fuzzy System Modeling Using VEGA (VEGA를 이용한 웨이브릿 기반 퍼지 시스템 모델링)

  • 이승준;주영훈;박진배
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2000.11a
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    • pp.149-152
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    • 2000
  • This paper addresses the wavelet fuzzy modeling using Virus-Evolutionary Genetic Algorithm (VEGA). We build a fuzzy system model which is equivalent to the wavelet transform after identifying the coefficients of wavelet transform. We can obtain an accurate system model with a small number of coefficients due to the energy compaction property of the wavelet transform. It thus means that we can construct a fuzzy system model with a small number of rules. In order to identify the wide-ranged coefficients of the wavelet transform, VEGA is adopted, which has prominent ability to avoid premature local convergence that is suitable to complex optimization problems. We demonstrate the superiority of our proposed fuzzy system modeling method over the previous results by modeling nonlinear function.

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Emotion Modeling for Emotion-based Personalization Service

  • Kim, Tae Yeun;Bae, Sang Hyun
    • Journal of Integrative Natural Science
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    • v.13 no.3
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    • pp.97-104
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    • 2020
  • This study suggests the emotion space modeling and emotion inference methods suitable for personalized services based on psychological and emotional models. For personalized emotion space modeling taking into account the subjective disposition based on the empirical assessment of the personal emotions felt by the personalization process of emotion space was used as a decision support tool, the Analytic Hierarchy Process. This confirmed that the special learning to perform personalized emotion space modeling without considering the subjective tendencies. In particular to check the possible reasoning based on fuzzy emotion space modeling and sensitivity for the quantification and vague human emotion to it based on the inherent human sensitivity.

Neuro-Fuzzy Approaches to Ozone Prediction System (뉴로-퍼지 기법에 의한 오존농도 예측모델)

  • 김태헌;김성신;김인택;이종범;김신도;김용국
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.6
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    • pp.616-628
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    • 2000
  • In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

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FUZZY IDENTIFICATION BY MEANS OF AUTO-TUNING ALGORITHM AND WEIGHTING FACTOR

  • Park, Chun-Seong;Oh, Sung-Kwun;Ahn, Tae-Chon;Pedrycz, Witold
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.701-706
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    • 1998
  • 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,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.

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Traffic Rout Choice by means of Fuzzy Identification (퍼지 동정에 의한 교통경로선택)

  • 오성권;남궁문;안태천
    • Journal of the Korean Institute of Intelligent Systems
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    • v.6 no.2
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    • pp.81-89
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    • 1996
  • A design method of fuzzy modeling is presented for the model identification of route choice of traffic problems.The proposed fuzzy modeling implements system structure and parameter identification in the eficient form of""IF..., THEN-.."", using the theories of optimization theory, linguistic fuzzy implication rules. Three kinds ofmethod for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 21,and proposed modified-linear inference (type 3). The fuzzy inference method are utilized to develop the routechoice model in terms of accurate estimation and precise description of human travel behavior. In order to identifypremise structure and parameter of fuzzy implication rules, improved complex method is used and the least squaremethod is utilized for the identification of optimum consequence parameters. Data for route choice of trafficproblems are used to evaluate the performance of the proposed fuzzy modeling. The results show that the proposedmethod can produce the fuzzy model with higher accuracy than previous other studies -BL(binary logic) model,B(production system) model, FL(fuzzy logic) model, NN(neura1 network) model, and FNNs (fuzzy-neuralnetworks) model -.fuzzy-neural networks) model -.

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