• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.663-667
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• 2004

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

• 한국국방경영분석학회지
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• 제26권2호
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• pp.55-74
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• 2000

A knowledge-based with production rules is a representation of static knowledge of an expert. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a method to reflect the dynamic nature of a system when we make inferences with a knowledge-based system. This paper suggests a strategy of dynamic inference that can be used to take into account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy production rules. A degree of match(DM) between actual input information and a condition of a rule is represented by a value [0,1]. Weights of relative importance of attributes in a rule are obtained by the AHP(Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected in an inference with fuzzy production systems.

• 한국컴퓨터정보학회논문지
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• 제18권9호
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• pp.43-51
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• 2013

신뢰성 있는 부정맥 진단을 위해서는 리듬 구간 및 심박 단위의 종합적인 분석을 통하여 심전도 신호에 대한 분류 결과가 제시되어야 한다. 본 논문에서는 심전도 신호의 특징점에 기반하여 규칙기반 분류를 이용한 일정 구간의 리듬 분석을 수행하고 SVM기반 분류를 이용한 심박 단위의 리듬분석을 첨가하였다. 규칙기반 분류에서는 리듬 구간의 특징에 대하여 임상 자료로부터 도출된 규칙 베이스를 이용하여 리듬 유형을 분류하도록 하며, SVM기반 분류에서는 심박 단위의 특징에 대하여 미리 학습된 다중 SVM 분류기를 이용하여 단조 리듬 및 주요 비정상 심박을 분류하도록 한다. MIT-BIH 부정맥 데이터베이스를 이용한 실험을 통하여 11가지 리듬 유형에 대하여 규칙기반 방법만을 적용하였을 경우 68.52%, 규칙기반과 SVM기반의 융합 방법을 적용하였을 경우 87.04%의 분류 성능을 각각 보였다. SVM기반 방법으로 단조 리듬과 배열 리듬에 대한 오분류 개선을 통하여 분류 성능에서 19% 정도가 향상됨을 확인하였다.

• KIEAE Journal
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• 제14권3호
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• pp.97-103
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• 2014

This study aimed at developing integrated logic for controlling heating device and openings of the double skin facade buildings. Two major logics were developed-rule-based control logic and artificial neural network based control logic. The rule based logic represented the widely applied conventional method while the artificial neural network based logic meant the optimal method. Applying the optimal method, the predictive and adaptive controls were feasible for supplying the advanced thermal indoor environment. Comparative performance tests were conducted using the numerical computer simulation tools such as MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation). Analysis on the test results in the test module revealed that the artificial neural network-based control logics provided more comfortable and stable temperature conditions based on the optimal control of the heating device and opening conditions of the double skin facades. However, the amount of heat supply to the indoor space by the optimal method was increased for the better thermal conditioning. The number of on/off moments of the heating device, on the other hand, was significantly reduced. Therefore, the optimal logic is expected to beneficial to create more comfortable thermal environment and to potentially prevent system degradation.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.145-150
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• 1997

In this paper is studied a method of fuzzy logic control based on possibly inconsistent if-then rules representing uncertain knowledge or imprecise data. In most cases of practical applications adopting fuzzy if-then rule bases, inconsistent rules have been considered as ill-defined rules and, thus, not allowed to be in the same rule base. Note, however, that, in representing uncertain knowledge by using fuzzy if-then rules, the knowledge sometimes can not be represented in literally consistent if-then rules. In this regard, when it is hard to obtain consistent rule base, we propose the weighted rule base fuzzy logic control depending on output performance using neural network and we will derive the weight update algorithm. Computer simulations show the proposed method has good performance to deal with the inconsistent rule base fuzzy logic control. And we discuss the real application problems.

• Structural Engineering and Mechanics
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• 제87권1호
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• pp.57-68
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• 2023

Response spectrum method is still an effective approach for the design of buildings with supplemental dampers. In practice, complex complete quadratic combination (CCQC) rule is always used in the response spectrum method to consider the effect of non-classical damping. The conventional CCQC rule is based on exact complex mode vectors. Sometimes the calculated complex mode vectors may be not excited by the external loading and errors in the structural responses always arise due to the mode truncation. Load-dependent Ritz (LDR) vectors are associated with the external loading and LDR vectors not excited can be automatically excluded. Also, contributions of higher modes are implicitly contained in the LDR vectors in terms of static responses. To improve the calculation efficiency and accuracy, LDR vectors are introduced in the CCQC rule in the present study. Firstly, the generation procedure of LDR vectors suitable for non-classical damping system is presented. Compared to the conventional LDR vectors, the LDR vectors herein are complex-valued and named as complex LDR (CLDR) vectors. Based on the CLDR vectors, the CCQC rule is then rederived and an improved response spectrum method is developed. Finally, the effectiveness of the proposed method in this paper is verified through three typical non-classical damping buildings. Numerical results show that the CLDR vector is superior to the complex mode with the same number in the calculation. Since the generation of CLDR vectors requires less computational cost and storage space, the method proposed in this paper offers an attractive alternative, especially for structures with a large number of degrees of freedom.

• 품질경영학회지
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• 제29권1호
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• pp.11-23
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• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• 한국정보처리학회논문지
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• 제4권9호
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• pp.2299-2308
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• 1997

대부분의 기계학습 방법들은 특정한 방법을 중심으로 연구되어 왔다. 하지만 두 가지 이상의 기계학습방법을 효과적으로 통합할 수 있는 방법에 대한 요구가 증가하며, 이에 따라 본 논문은 귀납법칙 (rule induction) 방법과 개체위주 학습방법 (instance-based learning)을 통합하는 시스템의 개발을 제시한다. 귀납법칙 단계에서는 엔트로피 함수의 일종인 Hellinger 변량을 사용하여 귀납법칙을 자동 생성하는 방법을 보이고, 개체위주 학습방법에서는 기존의 알고리즘의 단점을 보완한 새로운 개체위주 학습방법을 제시한다. 개발된 시스템은 여러 종류의 데이터에 의해 실험되었으며 다른 기계학습 방법과 비교되었다.

• 전자공학회논문지C
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• 제36C권3호
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• pp.47-56
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• 1999

한국어를 연속적으로 발음할 때 여러 가지 음은변동이 일어난다. 이러한 음운변동은 한국어 연속 음성 인식을 어렵게 하는 주요 요인 중의 한가지이다. 본 논문에서는 음운변동이 반영된 음성 인식 문자열을 규칙에 의하여 text 기반 문자열로 다시 복원시키는 rule-based 시스템을 제안한다. 그리고 복원 결과들은 형태소 분석되어 올바른 문자열만 생성된다. 복원은 4가지 rule 즉, 음절 경계 종성 초성 복원 rule, 모음처리 복원 rule,끝음절 종성 복원 rule, 한 음절 처리 rule에 의거하여 이루어진다. 규칙 적용 과정 중에 효과적인 복원을 위해 x-clustering정보를 정의하여 사용하고, 형태소 분석기에 입력될 복원 후보수를 제안하기 위해 postfix음절 빈도정보를 구하여 사용한다. 본 시스템은 규칙기반 시스템이므로 대용량의 발음열 사전이나 음소열 사전을 필요로 하지 않고 문서 기반 형태소 분석기를 그대로 이용할 수 있다는 이점이 있다.

• 제어로봇시스템학회논문지
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• 제4권5호
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• pp.624-629
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• 1998

Evolutionary computation techniques can solve search problems using simulated evolution based on the ‘survival of the fittest’. Recently, the genetic programming (GP) which evolves computer programs using the genetic algorithm was introduced. In this paper, the genetic programming technique is used in order to design a rule based controller consisting of condition-action rules for an unknown system. No a priori knowledge about the structure of the controller is needed. Representation of a solution, functions and terminals in GP are analyzed, and a method of constructing a fuzzy logic controller using the obtained rule based controller is described. A simulation example using a nonlinear system shows the validity and efficiency of the proposed method.