• 제목/요약/키워드: Combination Rule

검색결과 238건 처리시간 0.023초

유전학 기반 학습 환경하에서 분류 시스템의 성능 향상을 위한 엔-버전 학습법 (An N-version Learning Approach to Enhance the Prediction Accuracy of Classification Systems in Genetics-based Learning Environments)

  • 김영준;홍철의
    • 한국정보처리학회논문지
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    • 제6권7호
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    • pp.1841-1848
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    • 1999
  • 델보는 주어진 사례의 집합으로부터 이들 사례들을 분류할 수 있는 베이지안 분류 규칙들로 이루어진 규칙 집합을 습득하는 유전학 기반 귀납적 학습 시스템이다. 규칙 집합의 습득과정에서 델보가 당면하게 되는 한 가지 문제점은 학습 과정이 최적의 규칙 집합이 아닌 지역 최적치를 습득하고 종료하는 경우가 가끔 발생한다는 것이다. 다른 하나의 문제점은 훈련 사례에 대한 경우와는 달리 새로운 평가 사례에 대해 분류 성능이 현저히 저하되는 규칙 집합을 습득하는 경우가 가끔 발생한다는 것이다. 본 논문에서는 이러한 문제점을 해결하여 보다 성능이 향상된 분류 시스템을 구축하기 위한 기법으로 엔-버전 시스템을 구축함으로써 분류 시스템의 전체적인 성능을 향상시키는 기법이다. 엔-버전 학습법의 구현을 위해 다수의 규칙 집합을 이용하여 최종 분류 결과를 도출해 내기 위한 기법과 습득된 규칙 합들로부터 분류 시스템을 구축하기 위한 최적의 규칙 집합의 조합을 찾기 위한 기법을 제시하고 다수의 사례 집합을 이용하여 엔-버전 학습법이 델보의 학습 환경에 미치는 영향을 평가하였다.

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퍼지 시스템의 2계층 퍼지 시스템으로의 변환 방법 (A method of converting fuzzy system into 2 layered hierarchical fuzzy system)

  • 주문갑
    • 한국지능시스템학회논문지
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    • 제16권3호
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    • pp.303-308
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    • 2006
  • 본 논문에서는 다입력 퍼지 로직 시스템에서 생기는 퍼지 규칙수의 기하급수적 증가를 막기 위하여, 주어진 퍼지 시스템의 THEN 부분을 이용하여 퍼지 규칙 벡터를 정의하고, 이를 이용하는 2계층의 계층 퍼지 시스템으로 변환하는 방법을 제시한다. 여기에서, 1번째 계층에서는 주어진 퍼지 시스템으로부터 생성되는 일차독립의 퍼지 규칙 벡터를 사용하고, 2계층에서는 1계층에서 사용된 퍼지 규칙 벡터들의 선형 합을 사용한다. 변환된 2계층의 퍼지 시스템은 주어진 퍼지 시스템과 동일한 근사 능력을 가질 뿐 아니라, 더 적은 수의 퍼지 규칙을 가짐을 보인다.

동등 변환 2계층 퍼지 시스템의 규칙 자동 학습 (Automatic learning of fuzzy rules for the equivalent 2 layered hierarchical fuzzy system)

  • 주문갑
    • 한국지능시스템학회논문지
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    • 제17권5호
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    • pp.598-603
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    • 2007
  • 본 논문에서는 다입력 퍼지 시스템에서 생기는 퍼지 규칙수의 기하급수적 증가를 막기 위하여, 1번째 계층에서는 주어진 퍼지 시스템으로부터 선형 독립의 퍼지 규칙 벡터를 구성하여 사용하고, 2계층에서는 1계층에서 사용된 퍼지 규칙 벡터들의 선형합을 사용하는 동등 변환된 2계층 퍼지시스템 구조에서, steapest descent 알고리듬을 이용한 퍼지 규칙의 자동 학습을 다룬다. 학습 방법의 타당성을 보이기 위하여, 공과 막대 시스템을 제어하는 기존의 퍼지 시스템을 학습한 결과를 보인다.

유전알고리즘과 러프집합을 이용한 계층적 식별 규칙을 갖는 가스 식별 시스템의 설계 (Design of Gas Identification System with Hierarchically Identifiable Rule base using GAS and Rough Sets)

  • 조해파;방영근;이철희
    • 산업기술연구
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    • 제31권B호
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    • pp.37-43
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    • 2011
  • In pattern analysis, dimensionality reduction and reasonable identification rule generation are very important parts. This paper performed effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, this paper constructed the hierarchically identifiable rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, this paper demonstrated the effectiveness of the proposed methods by identifying five types of gases.

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Association Rule Mining by Environmental Data Fusion

  • Cho, Kwang-Hyun;Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • 제18권2호
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    • pp.279-287
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    • 2007
  • Data fusion is the process of combining multiple data in order to produce information of tactical value to the user. Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. In this paper, we develop was macro program for statistical matching which is one of five branch types for data fusion. And then we apply data fusion and association rule techniques to environmental data.

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High Accuracy Classification Methods for Multi-Temporal Images

  • Hong, Sun Pyo;Jeon, Dong Keun
    • The Journal of the Acoustical Society of Korea
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    • 제16권1E호
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    • pp.3-8
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    • 1997
  • Three new classification methods for multi temporal images are proposed. They are named as a likelihood addition method, a likelihood majority method and a Dempster-Shafer's rule method. Basic strategies using these methods are to calculate likelihoods for each temporal data and to combine obtained likelihoods for final classification. These three methods use different combining algorithms. From classification experiments, following results were obtained. The method based on Dempster-Shafer's rule of combination showed about 12% improvement of classification accuracies compared to a conventional method. This method needed about 16% more processing times than that of a conventional method. The other two proposed method showed 1% to 5% increase of classification accuracies. However processing times of these two proposed method showed 1% to 5% increase of classification accuracies. However processing times of these two methods are almost the same with that of a conventional method. Among the newly proposed three methods, the Dempster-Shafer's rule method showed the highest classification accuracies with more processing time than those of other methods.

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A Study on the Neuro-Fuzzy Control and Its Application

  • So, Myung-Ok;Yoo, Heui-Han;Jin, Sun-Ho
    • Journal of Advanced Marine Engineering and Technology
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    • 제28권2호
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    • pp.228-236
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    • 2004
  • In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

이중 후방 응력 경화 모델을 이용한 주기 하중에서의 래쳐팅 거동 현상 연구 (Simulation of Ratcheting Behavior under Stress Controlled Cyclic Loading using Two-Back Stress Hardening Constitutive Relation)

  • 홍성인;황두순;윤수진
    • 소성∙가공
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    • 제17권1호
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    • pp.19-26
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    • 2008
  • In the present work, the ratcheting behavior under uniaxial cyclic loading is analyzed. A comparison between the published and the results from the present model is also included. In order to simulate the ratcheting behavior, Two-Back Stress model is proposed by combining the non-linear Armstrong-Frederick rule and the non-linear Phillips hardening rule based on kinematic hardening equation. It is shown that some ratcheting behaviors can be obtained by adjusting the control material parameters and various evolutions of the kinematic hardening parameter can be obtained by means of simple combination of hardening rules using simple rule of mixtures. The ultimate back stress is also derived for the present combined kinematic hardening models.

도립진자 시스템의 뉴로-퍼지 제어에 관한 연구 (A Study on the Neuro-Fuzzy Control for an Inverted Pendulum System)

  • 소명옥;류길수
    • Journal of Advanced Marine Engineering and Technology
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    • 제20권4호
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    • pp.11-19
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    • 1996
  • Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.

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Dempster-Shafer 룰 결합을 이용한 변압기 보호계전 알고리즘 (Transformer Protective Relaying Algorithm Using A Dempster-Shafer'a Rule of Combination)

  • 강대훈;이승재;강상희;김상태;권태원;김일동;장병태;임성일
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1998년도 하계학술대회 논문집 C
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    • pp.1094-1096
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    • 1998
  • An intelligent power transformer protective relaying algorithm based on fuzzy decision-making is proposed. To distinguish external faults with CT saturation, overexcitation and inrush conditions from internal faults, a newly designed fuzzy-rule base is used. The Dempster-Shafer's rule of combition is used for fuzzy inference. A series of the S/W and H/W tests show the proposed protection algorithm has practically sufficient sensitivity and selectivity.

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