• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.86-89
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• 2000

In this paper, we consider a fuzzy system representation for polynomials of two variables. The representation we use is an exact transformation of the corresponding cubic spline interpolation function. We examine some of the properties of their fuzzy rule tables md prove that the rule table is symmetric or antisymmetric depending on whether the polynomial is symmetric or antisymmetric. A few examples are included to verify our proof. These results not only provide some insights on properties of the cubic spline interpolation coefficients but also provide some help in setting up fuzzy rule tables for functions of two variables.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.153-156
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• 2001

Fuzzy controllers also show good performance In case of the systems being nonlinear and difficult to solve. But these fuzzy controllers have problems which have to decide suitable rules and membership functions. In general we decide those using the heuristic methods or the experience of experts. Therefore, many researchers have applied genetic algorithms to make fuzzy rule automatically. In this paper, we suggest a new coding method and a new crossover method to maintain the good fuzzy rule base and the shape of membership

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(3)
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• pp.5-8
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• 2000

In this paper, we propose new algorithms for the partition of input space and the generation of fuzzy control rules. The one consists of Shannon and extended fuzzy entropy function, the other consists of adaptive fuzzy neural system with back propagation teaming rule. The focus of this scheme is to realize the optimal fuzzy rule base with the minimal number of the parameters of the rules, reducing the complexity of the system. The proposed algorithm is tested with the time series prediction problem using Mackey-Glass chaotic time series.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.1058-1062
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• 1993

A fuzzy reasoning method is proposed for the implementation of control systems based on non-fuzzy microprocessors. The essence of the proposed method is to search the local active miles instead of the global rule base. Thus the reasoning is conveniently performed on a master cell as a fuzzy accelerating kernel, which is transformed from an active fuzzy cell. The interpolative reasoning is simplified via adopting the algebraic product of fulfillment for the conditional connective AND and the weighted average for the rule sentence connective ALSO.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.701-706
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• 1998

A design method of rule -based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of " IF..., THEN,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.

• 전기학회논문지
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• 제65권9호
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• pp.1541-1550
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• 2016

In this paper, we introduce a novel learning methodology of fuzzy clustering-based neural network pattern classifier. Fuzzy clustering-based neural network pattern classifier depicts the patterns of given classes using fuzzy rules and categorizes the patterns on unseen data through fuzzy rules. Least squares estimator(LSE) or weighted least squares estimator(WLSE) is typically used in order to estimate the coefficients of polynomial function, but this study proposes a novel coefficient estimate method which includes advantages of the existing methods. The premise part of fuzzy rule depicts input space as "If" clause of fuzzy rule through fuzzy c-means(FCM) clustering, while the consequent part of fuzzy rule denotes output space through polynomial function such as linear, quadratic and their coefficients are estimated by the proposed local least squares estimator(LLSE)-based learning. In order to evaluate the performance of the proposed pattern classifier, the variety of machine learning data sets are exploited in experiments and through the comparative analysis of performance, it provides that the proposed LLSE-based learning method is preferable when compared with the other learning methods conventionally used in previous literature.

• Journal of Information Technology Applications and Management
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• 제12권4호
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• pp.1-12
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• 2005

In general, other conventional researches propose the fuzzy Petri net-based fuzzy reasoning algorithms based on the exhaustive search algorithms. If it can allow the certainty factors representing in the fuzzy production rules to use as the heuristic information, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more effective manner. This paper presents a fuzzy Petri net(FPN) model to represent the fuzzy production rules of a rule-based system. Based on the fuzzy Petri net model, a weighted fuzzy reasoning algorithm is proposed to Perform the fuzzy reasoning automatically, This algorithm is more effective and more intelligent reasoning than other reasoning methods because it can perform fuzzy reasoning using the certainty factors which are provided by domain experts as heuristic information

• 융합신호처리학회논문지
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• 제10권1호
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• pp.93-99
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• 2009

퍼지제어, 신경망, 유전알고리즘은 시스템의 지능을 좀 더 높게 만들기 위한 알고리즘들이다. 본 논문은 원하는 응답을 얻기 위해 유전알고리즘을 사용해서 퍼지제어기를 최적화시켰다. 또한, 보상된 퍼지제어기는 두 개의 제어규칙을 갖는다. 하나의 제어규칙은 오버슛과 과도응답영역에서 일어나는 상승시간을 감소시키기 위해 사용하고 다른 하나는 정상상태오차를 줄이고 수렴영역에서의 수렴을 빠르게 가져가기 위해 사용된다. 유전알고리즘 제어기는 두 개의 퍼지 룰 베이스의 최적한 교체시기를 찾기 위해 사용하며 퍼지-유전알고리즘 제어기는 재생산, 교배와 변이의 과정을 갖는다. 그리고 유압서보 모터 제어시스템에 적용하여 제안한 알고리즘을 실험하였다. 실험 결과 보상된 FUZZY-GA제어기가 두 개의 룰 베이스를 갖는 퍼지제어 기술에 비해 좋은 제어성능을 가짐을 관찰하였다.

• 융합신호처리학회논문지
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• 제8권3호
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• pp.222-226
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• 2007

본 연구에서는 응답의 과도영역에서 특징점의 각 영역과 $e-{\Delta}e$ 위상평면을 이용하여 이중 퍼지룰을 구성하였다. 구성된 퍼지 제어룰은 기본 퍼지제어 룰에 비해 상승시간, 지연시간, 최대오브슈트 등을 줄일 수 있었으며 퍼지 룰의 제안방법은 $e-{\Delta}e$ 위상평면에서 하나의 퍼지룰은 과도영역에서 정상상태오차를 일으키고 다른 퍼지 제어 룰은 정상상태오차를 감소시키고 수렴영역에서 빠르게 수렴하도록 교대로 사용한다. 또한 $e-{\Delta}e$ 위상평면에서의 두 개의 퍼지 룰은 플랜트의 응답 특성에 따라 교체시기를 결정한다. DSP 프로세서를 사용하는 유압서보 모터 제어시스템을 통한 실험의 결과로서 제안된 이중 퍼지 제어 룰은 기본 퍼지 제어 룰에 비해 빠른 상승시간, 정정시간등 양호한 응답을 얻을 수 있음을 확인하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.448-451
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• 2005

교통의 혼잡량이라든가 공기의 쾌적도 둥을 측정할 때는 상징적인 정보량을 이용한 퍼지 센서 알고리즘을 사용한다. 그런데 퍼지 센서를 구현할 때는 몇 개의 상징적인 정보량을 퍼지 규칙으로서 종합하여 출력을 산출하는데 상징적인 정보량을 퍼지 규칙이라는 막연한 방법을 사용하므로서 정확하지 못한 결과를 산출할 수밖에 없다. 따라서 본 논문에서는 퍼지 규칙으로 퍼지 센서를 구현하는 방법이 아닌 계층 분석 방법이라는 분석적인 방법을 이용하여 퍼지 센서를 구현하였고 이를 검증하기 위하여 퍼지 규칙 방법의 괴지 센서와 계층 분석 방법의 퍼지 센서를 교통량 제어에 적용하여 많은 통과차량수의 검증을 통하여 비교하여 보았다.