• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.761-763
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• 2008

최근 온라인교육의 필요성이 높아지고 요구 수준이 커짐에 따라 교육 서비스를 제공하는 시스템의 지능화된 처리능력이 필요하다. 퍼지신경회로망은 각각의 가중치(weight)를 갖는 채널로 연결한 망형태의 계산모델이다. 퍼지신경회로망을 학습시스템에 적용하여 학습자의 문항테스트 결과에서 학습과정을 재설정 할 수 있는 출력 값을 생성한다. 적응형 학습시스템은 퍼지신경회로망을 적용하여 개별화된 강의 코스로 학습을 진행하고 결과의 feedback을 통해 학습자의 최적 커리큘럼을 찾아내는 방법을 구현하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.1
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• pp.43-51
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• 1995

In this paper, we propose a neural network with fuzzy preprocessor not only for improving the classifi¬cation accuracy but also for being able to classify objects whose attribute values do not have clear bound¬aries. The fuzzy input signal representation scheme is included as a preprocessing module. It transforms imprecise input in linguistic form and precisely stated numerical input into multidimensional numerical values. 'The transformed input is processed in the postprocessing module. The experimental results indi-cate the superiority of fuzzy input signal representation scheme in comparison to binary input signal rep¬resentation scheme and decimal input signal representation scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.6-12
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• 2009

In this paper, we propose a system that extracts licence plate and recognizes numerals in the licence plate. The candidate area of licence plate is extracted using the improved IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. And the morphological filters are used to reduce noise from the extracted licence plate. The extracted licence plate is standardized using Hough transform and geometric transform. Backpropagation neural network is used to recognize numerals that are separated using the projection technique.

• 전기의세계
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• v.44 no.7
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• pp.17-23
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• 1995

인공지능 기법들은 여러 분야에 걸쳐 다양하게 사용되고 있으며, 특히 최근들어 지능형 제어기법으로 전력 전자 분야에도 많이 적용되고 있는 추세이다. 따라서 본 글에서는 전력전자 분야에 전문가 시스템, 신경회로망 및 퍼지논리를 적용한 예와 신경회로망과 퍼지논리의 융합에 대하여 정리 및 고찰해보고자 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.226-231
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• 2007

This paper presents a method of colored object extraction from an image using the fuzzy neural network. Fuzzy neural network divides an image into two clusters. It extracts the prototypes of Cb and Cr of object and background by controlling the vigilance parameter. The proposed method extracted object regardless of the position, the size, and the intensity of object. We compared the performance of the proposed method with that of the method of using subjective threshold value. And, we compared the performance of the proposed method with that of the method of using subjective threshold value by using several images with added noises.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.8-14
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• 2013

In this paper, a control system for two wheeled inverted pendulum robot is implemented to have more stable balancing capability than the conventional control system. Fuzzy control structure is chosen for the two wheeled inverted pendulum robot, and fuzzy membership function factors for the control system are obtained for 3 specified weights using a trial-and-error method. Next a neural network is employed to generate fuzzy membership function factors for more stable control performance when the weight is arbitrarily selected. Through some experiments, we find that the proposed fuzzy control system using the neural network is superior to the conventional fuzzy control system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.245-248
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• 2000

In this paper we improve the performance of autonomous mobile robot by induction of reinforcement learning concept. Generally, the system used in this paper is divided into two part. Namely, one is neural-fuzzy and the other is dynamic recurrent neural networks. Neural-fuzzy determines the next action of robot. Also, the neural-fuzzy is determined to optimal action internal reinforcement from dynamic recurrent neural network. Dynamic recurrent neural network evaluated to determine action of neural-fuzzy by external reinforcement signal from environment, Besides, dynamic recurrent neural network weight determined to internal reinforcement signal value is evolved by genetic algorithms. The architecture of propose system is applied to the computer simulations on controlling autonomous mobile robot.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.3
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• pp.53-61
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• 2006

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy nile as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• ICROS
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• v.1 no.3
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• pp.83-91
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• 1995

이 글에서는 신경회로망의 장점과 퍼지논리의 장점을 최대한 이용하며 각각의 단점을 보완하는 뉴로-퍼지 융합 기술과 현재 연구의 흐름을 간단히 살펴보았다. 비구조적인 정보 뿐만 아니라 구조적인 정보까지도 신경회로망의 영역 안에서 처리할 수 있는 새로운 뉴로-퍼지 회로망을 소개하였다. 소개한 뉴로-퍼지 회로망은 비퍼지화와 비퍼지화에 의해 발생하는 오차를 잘 보상할 수 있을 뿐만 아니라, 최적의 입출력 퍼지 소속 함수의 중심점과 모양을 찾을 수 있는 장점이 있다. 또한, 그 특성을 알지 못하는 임의의 비선형 동적 시스템에서 입출력 데이터만 얻을 수 있으며 시스템을 모델할 수 있는 퍼지 규칙을 언어적인 방법과 수치적인 방법으로 표현할 수 있으며 간단한 예제를 통한 시뮬레이션 결과를 보였다. 소개한 뉴로-퍼지 회로망을 이용하여 뉴로-퍼지 제어기를 구성할 수도 있으며, 또한 시스템의 역 퍼지 규칙을 찾는데 이용할 수도 있다. 향후 보다 우수한 일반화 성능을 가질 수 있는 뉴로-퍼지 회로망의 개발이 필요하며, 충분한 입출력 데이터를 얻는 방법의 연구도 필요하다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.3
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• pp.139-146
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• 1998

In this paper, we proposed the hybrid intelligent control method for fast response time and precise speed control of the AC Servo system. The proposed system first used the fuzzy-neural network control methods for fast response time and when the error reaches the preset value, used the PLL control method. In order to verify the advantage of he proposed method, the system is implemented. The results of the simulation and the experiment of speed control to use the 3-phase induction motor as a plant, we verified excellency of the proposed control method to compare with the conventional fuzzy-neural network control method.