• 제목/요약/키워드: Fuzzy Structure Modeling

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Automatic Generation of Fuzzy Rules using the Fuzzy-Neural Networks

  • Ahn, Taechon;Oh, Sungkwun;Woo, Kwangbang
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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    • pp.1181-1186
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    • 1993
  • In the paper, a new design method of rule-based fuzzy modeling is proposed for model identification of nonlinear systems. The structure indentification is carried out, utilizing fuzzy c-means clustering. Fuzzy-neural networks composed back-propagation algorithm and linear fuzzy inference method, are used to identify parameters of the premise and consequence parts. To obtain optimal linguistic fuzzy implication rules, the learning rates and momentum coefficients are tuned automatically using a modified complex method.

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하이브리드 신경망을 이용한 실내(室內) 쾌적감성(快適感性)모형 개발 (Development of Comfort Feeling Structure in Indoor Environments Using Hybrid Neuralnetworks)

  • 전용웅;조암
    • 대한인간공학회지
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    • 제20권2호
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    • pp.29-46
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    • 2001
  • This study is about the modeling of comfort feeling structure in indoor environments. To represent the degree of practical comfort feeling level in an environment, we measured elements of human sense and resultant elements of comfort feeling such as coziness, refreshment, and freshness with physical values(temperature, illumination, noise. etc.). The relationships of elements of human sense and elements of comfort feeling were formulated as a fuzzy model. And a hybrid-neural network with three layers were designed where obtained from fuzzy membership function values of the elements of human sense were used as inputs, and given as fuzzy membership function values of resultant elements of comfort feeling were used as outputs. Both kinds of fuzzy membership function values were obtained from physical values. The network was trained by measured data set. The proposed hybrid-neural network were tested and proposed a more realistic model of comfort feeling structure in indoor environments.

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A New Fuzzy Logic based Modeling and Simulation of a Switched Reluctance Motor

  • Wadnerkar, Vikas S.;Bhaskar, Mithun M.;Das, Tulasi Ram;RajKumar, A.D.
    • Journal of Electrical Engineering and Technology
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    • 제5권2호
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    • pp.276-281
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    • 2010
  • The switched reluctance motor (SRM) is an older member of the electric machines family. Its simple structure, ruggedness and inexpensive manufacturing potential make it extremely attractive for industrial applications. However, these merits are overshadowed by its inherent high torque ripple, acoustic noise and difficulty to control. In this paper, a control strategy of the angle position control for the SRM drive based on fuzzy logic is illustrated. The input control parameter, the output control parameter and fuzzy control with FAM table formulation strategy are described and simulated with control patterns, and the decision form of the fuzzy control is illustrated and simulated, and the scope of implementing in a Fuzzy based ASIC chip is enlightened with literature support.

현장근로자 핵심역량의 의식구조에 대한 퍼지분석 (Fuzzy Analysis for Consciousness Structure of Core Competency of Manufacturing Workers)

  • 기종대;황승국
    • 한국지능시스템학회논문지
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    • 제21권3호
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    • pp.378-382
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    • 2011
  • 본 논문에서는 제조업에 종사하는 현장근로자의 핵심역량을 개발하고 이 핵심역량에 대한 의식구조를 분석한다. 의식구조의 분석방법으로 일반적으로는 ISM과 FSM을 각각 사용하여 층을 분류하고 그 연결 상태를 파악하게 된다. 그러나, 데이터에 따라 각 층의 요인들이 달라지는 경우가 많이 발생하게 되는데 이것은 기본적으로 구조는 정해져있고 그 연결고리가 방법에 따라 달라질 수 있다는 관점에서 본 논문에서는 ISM을 통하여 먼저 구조모델을 결정하고, 연결고리는 FSM으로 결정하는 방법을 제시하고자 하였다. 이 방법을 이용하여 제조업의 현장관리자의 핵심역량에 대한 의식구조를 분석하는데 전문가의 확인을 통해 보다 객관성 있는 구조모델을 제시하였다.

Optimal Learning of Neo-Fuzzy Structure Using Bacteria Foraging Optimization

  • Kim, Dong-Hwa
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.1716-1722
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    • 2005
  • Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

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Fuzzy control for geometrically nonlinear vibration of piezoelectric flexible plates

  • Xu, Yalan;Chen, Jianjun
    • Structural Engineering and Mechanics
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    • 제43권2호
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    • pp.163-177
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    • 2012
  • This paper presents a LMI(linear matrix inequality)-based fuzzy approach of modeling and active vibration control of geometrically nonlinear flexible plates with piezoelectric materials as actuators and sensors. The large-amplitude vibration characteristics and dynamic partial differential equation of a piezoelectric flexible rectangular thin plate structure are obtained by using generalized Fourier series and numerical integral. Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear structural system, which combines the fuzzy inference rule with the local linear state space model. A robust fuzzy dynamic output feedback control law based on the T-S fuzzy model is designed by the parallel distributed compensation (PDC) technique, and stability analysis and disturbance rejection problems are guaranteed by LMI method. The simulation result shows that the fuzzy dynamic output feedback controller based on a two-rule T-S fuzzy model performs well, and the vibration of plate structure with geometrical nonlinearity is suppressed, which is less complex in computation and can be practically implemented.

MEMS기반 에너지 하베스터 제작을 위한 실리콘 KOH 식각 모형화 (Modeling of Silicon Etch in KOH for MEMS Based Energy Harvester Fabrication)

  • 민철홍;강경우;김태선
    • 한국전기전자재료학회논문지
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    • 제25권3호
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    • pp.176-181
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    • 2012
  • Due to the high etch rate and low fabrication cost, the wet etching of silicon using KOH etchant is widely used in MEMS fabrication area. However, anisotropic etch characteristic obstruct intuitional mask design and compensation structures are required for mask design level. Therefore, the accurate modeling for various types of silicon surface is essential for fabrication of three-dimensional MEMS structure. In this paper, we modeled KOH etch profile for MEMS based energy harvester using fuzzy logic. Modeling results are compared with experimental results and it is applied to design of compensation structure for MEMS based energy harvester. Through Fuzzy inference approaches, developed model showed good agreement with the experimental results with limited etch rate information.

바이러스-진화 유전 알고리즘을 이용한 퍼지 모델링 (Fuzzy Modeling Using Virus-Evolutionary Genetic Algorithm)

  • 이승준;주영훈;박진배
    • 한국지능시스템학회논문지
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    • 제10권5호
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    • pp.432-441
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    • 2000
  • 본 논문은 기존의 수학적인 모델링으로는 만족스러운 결과를 얻기 어려운 복잡하고 불확실한 비선형 시스템에 대한 퍼지 모델링 기법을 다룬다. 유전 알고리듬은 어느 정도 최적해를 전역적으로 찾을 수 있기 때문에 퍼지 모델링시에 파라미커와 구조를 동정하기 위하여 사용되었다. 하지만, 유전 알고리듬은 개체군이 유전적 다양성을 잃었을 경우 조기 수렴한다는 문제점이 있으며 바이러스-진화 유전 알고리듬은 이러한 지역수렴에 대한 방아닝 될 수 있다. 따라서, 본 논문에서는 바이러스 이론이 적용된 VEGA를 퍼지 모델링 할 때 이용할 수 있는 방법을 제안한다. 이 방법에서는 지역정보가 개체군 내에서 교환됨으로써 유전적 다양성을 유지하게 된다. 마지막으로, 본 논문에서 제안한 방법의 우수성과 일반성을 평가하기 위해 몇 가지의 수치적 예제를 제공한다.

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다중모델기법을 이용한 비선형시스템의 퍼지모델링 (Fuzzy Modeling for Nonlinear System Using Multiple Model Method)

  • 이철희;하영기;서선학
    • 산업기술연구
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    • 제17권
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    • pp.323-330
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    • 1997
  • In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure, and the mountain clustering technique is used in partitioning of system. TSK rule structure is adopted to form the fuzzy rules, and Back propagation algorithm is used for learning parameters in consequent parts of the rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators. Computer simulations are performed to verify the effectiveness of the proposed method.

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비선형 시스템의 퍼지 모델링 및 제어 (An Approach to Fuzzy Modeling and Control of Nonlinear Systems)

  • 이철희;하영기;서선학
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1997년도 하계학술대회 논문집 B
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    • pp.425-427
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    • 1997
  • In this paper, a new approach to modeling and control of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure. The mountain clustering technique is used in partitioning of system, and TSK rule structure is adopted to form the fuzzy rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators.

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