• 제목/요약/키워드: structure inference

검색결과 412건 처리시간 0.033초

A note on the geometric structure of the t-distribution

  • Cho, Bong-Sik;Jung, Sun-Young
    • Journal of the Korean Data and Information Science Society
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    • 제21권3호
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    • pp.575-580
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    • 2010
  • The Fisher information matrix plays a significant role in statistical inference in connection with estimation and properties of variance of estimators. In this paper, the parameter space of the t-distribution using its Fisher's matrix is de ned. The ${\alpha}$-scalar curvatures to parameter space are calculated.

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

  • 안상철;권욱현
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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    • pp.484-489
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    • 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.

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고속 온라인 적응기능을 갖는 비선형 적응등화기 (A nonlinear adaptive equalizer with fast on-line adaptation)

  • 오덕길;최진영;이충웅
    • 전자공학회논문지A
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    • 제32A권8호
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    • pp.11-18
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    • 1995
  • This paper proposes a nonlinear adaptive equalizer which is based on fuzzy rules and fuzzy inference of several affine mapping for the received channel data. The proposed nolonlinear adaptive equalizers with the significantly lower computational complexity. Also it can be applied to the on-line adaptation environments owing to its fast convergence characteristics and the lower computational load. When using the decision feedback vectors, this equaalizer can be easily realized in the form of the DFE structure with out the requirement for the perfect channel knowledge as in the case of the fuzzy adaptive filter.

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로봇 매니퓰레이터를 위한 퍼지 이동 슬라이딩 모드 제어 (Fuzzy moving sliding mode control for robotic manipulators)

  • 한태열;전경한;최봉열
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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    • pp.348-348
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    • 2000
  • In this paper, we present a fuzzy moving sliding mode control for two-degrree-of-freedom robotic manipulator. 17he sliding surface parameters are designed by fuzzy inference. The proposed sliding mode control makes the error always remain on the surface from beginning and therefore, the system is insensitive to system uncertaintics and external disturbances. Simulation results show the effectiveness of proposed scheme.

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퍼지기법을 이용한 공장자동화용 토큰버스 네트워크의 성능관리 (Development of Fuzzy Network Performance Manager for Token Bus Factory Automation Networks)

  • 이상오
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 1995년도 춘계학술대회 논문집
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    • pp.471-476
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    • 1995
  • This paper focues on development and implementation of a perfomance management algorithm for IEEE802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisfy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents the structure of a network performance manager that possesses the knowledge about perfomance management in a set of fuzzy rules and deriving its action through fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.

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유니사이클 로봇의 곡선경로 추종을 위한 퍼지규칙 (Fuzzy Rule for Curve Path Tracking of a Unicycle Robot)

  • 김중완;정희균
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 1996년도 추계학술대회 논문집
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    • pp.425-429
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    • 1996
  • Our unicycle has simple mechanical structure. But unicycle's dynamic system is a very sensitive unstable nonlinear system. In this paper, a fuzzy inference control mechanism was established throughout an inquiry into human riding a unicycle, and we developed a direct fuzzy controller to control our unicycle robot. This proposed fuzzy controller is consisted with fuzzy logic controllers for attitude stability and wheel's velocity. Computer simulation results show that our fuzzy controller has very powerful performance to unstable nonlinear unicycle robot system.

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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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FUZZY METHOD FOR FINDING THE FAULT PROPAGATION WAY IN INDUSTRIAL SYSTEMS

  • Vachkov, Gancho;Hirota, Kaoru
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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    • pp.1114-1117
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    • 1993
  • The paper presents an effective method for finding the propagation structure of the real origin of a system malfunction. It uses a combined system model consisting of Structural Model (SM) in the form of Fuzzy Directed Graph and Behavior Model (BM) as a set of Fuzzy Relational Equations $A\;{\circ}\;R\;=\;B$. Here a specially proposed fuzzy inference technique is checked and investigated. Finally a test example for fault diagnosis of an industrial system is given and analyzed.

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Architecture of a PDM VLSI Fuzzy Logic Controller with an Explicit Rule Base

  • Ungering, Ansgar P.;Goser, K.
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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    • pp.1386-1389
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    • 1993
  • We are describing the architecture of a fuzzy logic controller using pulse-width-modulation (PDM) technique and a pipeline structure. Features of this controller are: A new architecture for the inference unit, reduced chip area and less I/O-pins. Additionally we present two different rule-bases: one hardwired with reduced chip-area and the other programmable for prototyping. Also an architecture of a parallel minimum-gate is shown.

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Genetically Optimized Hybrid Fuzzy Neural Networks Based on Linear Fuzzy Inference Rules

  • Oh Sung-Kwun;Park Byoung-Jun;Kim Hyun-Ki
    • International Journal of Control, Automation, and Systems
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    • 제3권2호
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    • pp.183-194
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    • 2005
  • In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.