• Title/Summary/Keyword: rule

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Reference Model Following Self-Organizing Controller (기준모델 추종 자기 구성 제어기)

  • Kwon, Choon-Ki;Bae, Sang-Wook;Park, Tae-Hong;Park, Gwi-Tae
    • Proceedings of the KIEE Conference
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    • 1993.11a
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    • pp.329-331
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    • 1993
  • A new RMFSOC(Reference Model Following Self-Organizing Controller) is proposed. It is composed by adding the reference model and decision rule to the Mamdani's SOC. The reference model is introduced to explicitly specify the control performance. The self-organizing level of the RMFSOC organizes the control rule which makes the process output follow the reference output generated by the reference model. In order to avoid unnecessary control rule modification, a decision rule is also introduced to determine whether the control rule modification is needed or not.

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The Performance Evaluation of Fuzzy Rule-Based System (퍼지 규칙기반제어기에서 시스템의 성능평가)

  • Kim, Young-Chul;Choi, Jong-Soo;Choi, Han-Soo;Kim, Sung-Joong
    • Proceedings of the KIEE Conference
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    • 1992.07a
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    • pp.261-264
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    • 1992
  • In designing the fuzzy rule-based system, it has effected by the four significant factors such as the choice of membership function, scaling factor, the numbers of fuzzy control rule, the method of defuzzification. In this paper we design the fuzzy rule based system and evaluate by three factors, as followes reaching time, overshoot, and amplitude. And then we wiII show that the significant factors are the choice of scaling factor and the numbers of fuzzy control rule, and the system performance can be improved by the proper selection of the scaling factors.

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A New Evolutionary Programming Algorithm using the Learning Rule of a Neural Network for Mutation of Individuals (신경회로망의 학습 알고리듬을 이용하여 돌연변이를 수행하는 새로운 진화 프로그래밍 알고리듬)

  • 임종화;최두현;황찬식
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.36C no.3
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    • pp.58-64
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    • 1999
  • Evolutionary programming is mainly characterized by two factors; one is the selection strategy and the other the mutation rule. In this paper, a new mutation rule that is the same form of well-known backpropagation learning rule of neural networks has been presented. The proposed mutation rule adapts the best individual's value as the target value at the generation. The temporal error improves the exploration through guiding the direction of evolution and the momentum speeds up convergence. The efficiency and robustness of the proposed algorithm have been verified through benchmark test functions.

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Design of Fuzzy Controller with dual control rules using $e-{\Delta}e$ phase plane ($e-{\Delta}e$ 위상평면을 이용한 이중 제어규칙을 갖는 퍼지 제어기 설계)

  • 박광묵;신위재
    • Proceedings of the IEEK Conference
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    • 1999.11a
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    • pp.1149-1152
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    • 1999
  • In this paper we analyzed each region of specific points and e-Δephase plane in order to make fuzzy rule base. After we composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. The composed method are converged more rapidly than single rule base in convergence region. Proposed method is alternately use at specific points of e-Δephase plane with two fuzzy control rules, that is one control rule occruing the steady state error used in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Two fuzzy control rules in the e-Δe phase plane decide the change time according to response characteristics of plants. As the results of simulation through the second order plant and the delay time plan, Proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

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On an Equal Mean Quadratic Classification Rule With Unknown Prior Probabilities

  • Kim, Hea-Jung;Inada, Koichi
    • Journal of Korean Society for Quality Management
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    • v.23 no.3
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    • pp.126-139
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    • 1995
  • We describe a formal approach to the construction of optimal classification rule for the two-group normal classification with equal population mean problem. Based on the utility function of Bernardo, we suggest a balanced design for the classification and construct the optimal rule under the balanced design condition. The rule is characterized by a constrained minimization of total risk of misclassification, the constraint of which is constructed by the process of equation between expected utilities of the two group conditional densities. The efficacy of the suggested rule is examined through numerical studies. This indicates that, in case little is known about the relative population sizes, dramatic gains in accuracy of classification result can be achieved.

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Design and Implementation of Rule-based Mask Layout Transformation System (규칙에 기초한 마스크 레이아웃 변환 시스템의 설계 및 구현)

  • 이재황;전주식
    • Journal of the Korean Institute of Telematics and Electronics A
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    • v.30A no.9
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    • pp.46-58
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    • 1993
  • Owing to the nature of locality in mask layouts, it appears that most mask layout problems can be solved by transforming a part of the given mask layout into a better layout segment continuously toward a global suboptimal solution. This notion of local transformation addresses major weak points of existing mask layout processing systems, which lack both extensibility and unifiability. This paper attempts to elaborate upon developing a new rule-based mask layout transformation system wherein most of the mask layout problems can be solved under the unified framework of local mask layout transformation. The rule-based mask layout transformation system is applicable to various mask layout problems such as net extraction, mask layout compaction, mask layout editing, and design rule checking. The experimental results show that the rule-based expert system approach is an efficient means of solving those mask layout problems, and thus confronting major drawbacks of existing layout processing systems.

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THE TRAPEZOIDAL RULE WITH A NONLINEAR COORDINATE TRANSFORMATION FOR WEAKLY SINGULAR INTEGRALS

  • Yun, Beong-In
    • Journal of the Korean Mathematical Society
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    • v.41 no.6
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    • pp.957-976
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    • 2004
  • It is well known that the application of the nonlinear coordinate transformations is useful for efficient numerical evaluation of weakly singular integrals. In this paper, we consider the trapezoidal rule combined with a nonlinear transformation $\Omega$$_{m}$(b;$\chi$), containing a parameter b, proposed first by Yun [14]. It is shown that the trapezoidal rule with the transformation $\Omega$$_{m}$(b;$\chi$), like the case of the Gauss-Legendre quadrature rule, can improve the asymptotic truncation error by using a moderately large b. By several examples, we compare the numerical results of the present method with those of some existing methods. This shows the superiority of the transformation $\Omega$$_{m}$(b;$\chi$).TEX>).

A Rule Termination Analyser in Active DBMS (능동형 데이터베이스 시스템에서의 규칙 종료 분석기)

  • Kim, Hong-K.;Park, In-S.;Hyun, Soon-J.
    • Proceedings of the Korea Information Processing Society Conference
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    • 2000.10a
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    • pp.7-10
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    • 2000
  • 능동형 데이터베이스 시스템은 사용자가 정의한 rule의 집합이 해당 event 가 발생하는 순간 능동적으로 일련의 행위를 수행하도록 정의된 시스템이다. 그러나. 이처럼 서로 긴밀한 관계를 갖는 rule들이 능동적으로 수행되는 과정에 종료되지 않고 무한히 순환하여 수행하는 경우가 발생할 수 있다. 이처럼 무한히 순환하여 수행할 수 있는 가능성을 분석하는 것이 Termination Analysis 이다. 본 논문은 compile time 에 rule 의 termination 을 예측하는 방법에 대한 연구로, Java Language 를 rule definition language로 사용하며, composite event의 경우도 지원하도록 기존의 Termination analysis 방법을 확장하였다.

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Fuzzy Modeling by Genetic Algorithm and Rough Set Theory (GA와 러프집합을 이용한 퍼지 모델링)

  • Joo, Yong-Suk;Lee, Chul-Heui
    • Proceedings of the KIEE Conference
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    • 2002.11c
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    • pp.333-336
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    • 2002
  • In many cases, fuzzy modeling has a defect that the design procedure cannot be theoretically justified. To overcome this difficulty, we suggest a new design method for fuzzy model by combining genetic algorithm(GA) and mush set theory. GA, which has the advantages is optimization, and rule base. However, it is some what time consuming, so are introduce rough set theory to the rule reduction procedure. As a result, the decrease of learning time and the considerable rate of rule reduction is achieved without loss of useful information. The preposed algorithm is composed of three stages; First stage is quasi-optimization of fuzzy model using GA(coarse tuning). Next the obtained rule base is reduced by rough set concept(rule reduction). Finally we perform re-optimization of the membership functions by GA(fine tuning). To check the effectiveness of the suggested algorithm, examples for time series prediction are examined.

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Splitting Algorithm Using Total Information Gain for a Market Segmentation Problem

  • Kim, Jae-Kyeong;Kim, Chang-Kwon;Kim, Soung-Hie
    • Journal of the Korean Operations Research and Management Science Society
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    • v.18 no.2
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    • pp.183-203
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    • 1993
  • One of the most difficult and time-consuming stages in the development of the knowledge-based system is a knowledge acquisition. A splitting algorithm is developed to infer a rule-tree which can be converted to a rule-typed knowledge. A market segmentation may be performed in order to establish market strategy suitable to each market segment. As the sales data of a product market is probabilistic and noisy, it becomes necessary to prune the rule-tree-at an acceptable level while generating a rule-tree. A splitting algorithm is developed using the pruning measure based on a total amount of information gain and the measure of existing algorithms. A user can easily adjust the size of the resulting rule-tree according to his(her) preferences and problem domains. The algorithm is applied to a market segmentation problem of a medium-large computer market. The algorithm is illustrated step by step with a sales data of a computer market and is analyzed.

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