• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권2호
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• pp.95-101
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권1호
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• pp.52-57
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• 2006

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.187-192
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• 2004

In this paper, we proposed the method to design fuzzy controller using the experience of the operating expert and experimental numeric data for the robust control about the noise and disturbance instead of the traditional PID controller for the main steam temperature control of the thermal power plant. The temperature of main steam temperature process has to be controlled uniformly for the stable electric power output. The process has the problem of the hunting for the cases of various disturbances. In that case, the manual action of the operator happened to be introduced in some cases. We adopted the TSK (Takagi-Sugeno-Kang) model as the fuzzy controller and designed the fuzzy rules using the informations extracted directly from the real plant and various operating condition to solve the above problems and to apply practically. We implemented the real fuzzy controller as the Function Block module in the DCS(Distributed Control System) and evaluated the feasibility through the experimental results of the simulation.

• 산업기술연구
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• 제31권B호
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• pp.45-50
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• 2011

Recently, machine olfactory systems that have been proposed as an artificial substitute of the human olfactory system are being studied by many researchers because they can scent dangerous gases and identify the type of gases in contamination areas instead of the human. In this paper, we present an effective design method for the gas identification system. The design method adopted the sequential combination between genetic algorithms and TSK fuzzy logic system. First, the proposed method allowed the designed gas identification system effectively performing the pattern analysis because it was able to avoid the curse of dimensionality caused by use of a large number of sensors. Secondly, the method led the gas identification system to good performance because it was able to deal with drift characteristics of the sensor data by using description ability of the fuzzy system for nonlinear data. In simulation, we demonstrated the effectiveness of the designed gas identification system by using the simulation results of five types of gases.

• 한국지능시스템학회논문지
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• 제17권1호
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• pp.52-57
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• 2007

본 논문은 실시간 음성인식명령으로 구동되는 지능형 이동기제어시스템을 제안한다. 제안된 이동기제어시스템은 메인모듈, 음성인식모듈, 모터구동모듈, 센서모듈로 구성된다. 80C196KC로 구현된 메인모듈에서 퍼지논리가 적용된 지능형 제어시스템을 제안한다. 사용자의 몸무게 및 여러 가지 주변 환경요인들에 의한 비선형성을 개선하기 위해서 피드백제어가 가능한 모터구동모듈과 센서모듈이 구현된다. 또한 제안된 시스템에서 이동로봇의 제어를 위한 9개의 단어를 사용하여 동작을 테스트하였고, 제어입력으로 음성명령과 조이스틱 사용 시 이동로봇의 성능을 평가하였다.