• 컴퓨터교육학회논문지
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• 제6권4호
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• pp.95-113
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• 2003

본 논문에서는 기존의 LCMS에서 사용되는 평가시스템에 퍼지 추론 규칙을 이용한 적응형 퍼지평가시스템(AFES ; Adaptie Fuzzy Evaluation System)을 제안한다. AFES 는 학습자가 하나의 학습코스(learning course)에 들어가기 전에 퍼지진단평가(fuzzy diagnostic evealuation)를 통해 학습자에게 코스수준(course level)을 부여한다. 학습자는 코스수준에 따른 맞춤식 학습경로(learning path)로 학습을 종료한 후, 퍼지최종평가(fuzzy final evaluation)를 통해 최종성적(final grade)을 AFES 으로부터 부여 받는다. AFES의 가장 큰 특징은 최종성적의 점수 부여 규칙에 있는데, 만약 서로 다른 학습자가 동일한 문제 수에 대하여 같은 수의 정답을 냈더라도, AFES 는 125 가지 퍼지 추론 규칙(fuzzy reasoning rule)에 의거하여 탄력적으로 서로 다른 최종성적을 학습자에게 부여한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.43-46
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• 2001

We, in this paper, design the Sugeno-models fuzzy controller by using the membership function modification algorithm and ANFIS, which are clustering and learning the input-output data. The membership function modification algorithm constructs the more concrete fuzzy controller by clustering the input-output data from the fuzzy inference system. ANFIS construct the Sugeno-models fuzzy controller by learning the input-output data from the above controller. We showed that the fuzzy controller designed by our method could have the stable learning and the enhanced performance.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.977.1-1000
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• 1993

This paper presents a fuzzy neural network, called the fuzzy counterpropagation network, that structures its inputs and generates its outputs in a manner based on counterpropagation networks. The fuzzy counterpropagation network is developed by incorporating the concept of fuzzy clustering into the hidden layer responses. Three learning algorithms are introduced for use with the proposed network. Simulations demonstrate that fuzzy counterpropagation networks with the proposed learning algorithms work well on approximating bipolar and continuous functions.

• 정보처리학회논문지B
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• 제10B권6호
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• pp.587-592
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• 2003

강화학습을 적용하기에 적합한 많은 실세계의 제어 문제들은 연속적인 상태 또는 행동(continuous states or actions)을 갖는다. 연속 값을 갖는 문제인 경우, 상태공간의 크기가 거대해져서 모든 상태-행동 쌍을 학습하는데 메모리와 시간상의 문제가 있다. 이를 해결하기 위하여 학습된 유사한 상태로부터 새로운 상태에 대한 추측을 하는 함수 근사 방법이 필요하다. 본 논문에서는 1-step Q-learning의 함수 근사를 위하여 퍼지 클러스터링을 기초로 한 Fuzzy Q-Map을 제안한다. Fuzzy Q-Map은 데이터에 대한 각 클러스터의 소속도(membership degree)를 이용하여 유사한 상태들을 군집하고 행동을 선택하고 Q값을 참조했다. 또한 승자(winner)가 되는 퍼지 클러스터의 중심과 Q값은 소속도와 TD(Temporal Difference) 에러를 이용하여 갱신하였다. 본 논문에서 제안한 방법은 마운틴 카 문제에 적용한 결과, 빠른 수렴 결과를 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1254-1259
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• 한국지능시스템학회논문지
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• 제15권7호
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• pp.805-812
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• 2005

본 논문은 스마트 홈과 같이 다양한 센서 및 제어 네트워크가 밀집되어 있는 유비쿼터스 환경 하에서 복잡한 인터페이스의 사용에 대한 사용자의 인지 부담(cognitive load)을 줄이고, 개인화된(personalized) 서비스를 자율적으로 제공하기 위한 새로운 사용자 행동 패턴 선호도 학습기법을 제안하였다. 이를 위해 지식 발견(knowledge discovery)을 위한 평생 학습(life-long learning)의 관점에서 퍼지귀납(fuzzy inductive) 학습 방법론을 제안하며, 이것은 수치 데이터로부터 입력 공간에 대한 효율적인 퍼지 분할(fuzzy partition)을 얻어내고 일관성 있는(consistent) 퍼지 상관 롤(fuzzy association rule)을 얻어내도록 한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1296-1301
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.303-308
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.387-390
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• 1998

In this paper, we suggested a neuro-fuzzy learning algorithm for tuning fuzzy rules, in which a fuzzy system model is of additive-type. Using the method, it is possible to reduce the computation size, since performing the fuzzy inference and tuning the fuzzy rules of each fuzzy subsystem model are independent. Moreover, the efficiency of suggested method is shown by means of a numerical example.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.358-364
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• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.