• 한국지능시스템학회논문지
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• 제6권3호
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• pp.40-50
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• 1996

By using the neural networks(NN) as system identifier, the on-line self tuning method for fuzzy controller(FC) is proposed. In theis method, the learning of NN is carried out during control operation of FC and the cinsequent parameters of FC is tuned on-line automatically by means of system output errors backpropagated through NN. The Sugeno fuzzy model with constants as consequent parameters is selected for simplifying computation. In procedures of parameter tuning, the gradient descent method is used and the gradient vectors for adjusting the weight of NN are transferred as controller output errors. To evaluate the performance, the proposed method is applied to the inverted pendulum system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1021-1024
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• 1993

System identification is a major component for a control system. In biosystems, which is nonlinear and dynamic, precise identification would be very helpful for implementing a control system. It is difficult to precisely identify such non-linear systems. The measurable data on products from 2,3-butanediol fermentation could not be included in a process model based on kinetic approach. Meanwhile, a predictive capability is required in developing a control system. A neural network (NN) dynamic identifier with a by/(1＋ t ) transfer function was therefore designed being able to predict this fermentation. This modified inverse NN identifier differs from traditional models in which it is not only able to see but also able to predict the system. A moving window, with a dimension of 11 and a fixed data size of seven, was properly designed. One-step ahead identification/prediction by an 11-3-1 BPNN is demonstrated. Even under process fault, this neural network is still able to perform several-step ahead prediction.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.259-267
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• 2014

This paper presents a simple, consistent and reliable technique to identify detected partial discharges (PD) using an acoustic ultrasonic method. A new reliable algorithm named 'Simple Partial Discharge Identifier' (SPDI) is proposed to perform identification process of the detected ultrasonic signals of PD. Experimental works based on recommended practices were setup and the ultrasonic signals of the PD were recorded. The PD data is then employed as the reference data. The SPDI developed has been tested against commonly used models in Neural Network (NN). Results from the SPDI algorithm shows more reliable results compared to NN models results. Comparison made on the mean square error (MSE) results shows SPDI produces the desired outcome with lower MSE in 97.17% of trials. Low error of SPDI indicates a high reliability to be applied in the identification of PD.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1137-1147
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• 2004

In this paper, we proposed two types of adaptive control mechanism which is named HIA(Humoral Immune Algorithm) PID and CMIA(Cell-Mediated Immune Algorithm) controller based on biological immune system under engineering point of view. The HIA PID which has real time control scheme is focused on the humoral immunity and the latter which has the self-tuning mechanism is focused on the T-cell regulated immune response. To verify the performance of the proposed controller, some experiments for the control of AGV which is used for the port automation to carry container without human are performed. The experimental results for the control of steering and speed of an AGV system illustrate the effectiveness of the proposed control scheme. Moreover, in that results, proposed controllers have better performance than other conventional PID controller and intelligent control method which is the NN(neural network) PID controller.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2002년도 추계공동학술대회논문집
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• pp.113-119
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• 2002

최근에, 자동화 크레인 제어 시스템은 빠른 속도와 신속한 수송이 요구되어 지고 있다 컨테이너 야드 내에서 크레인 시스템의 동작 동안, 스프레더에 매달린 컨테이너의 흔들림은 최소화로 되도록 크레인의 트롤리 위치와 와이어 로프 길이 제어가 필요하다. 크레인 시스템에서 자동 주행 제어 기술과 흔들림 방지 기술을 사용하여 무인 자동화 제어 시스템의 개발을 할 수 있는 핵심 기술이다. 그 결과 우리는 트랜스퍼 크레인 시스템 제어에서 자동 주행 제어를 위한 제어기를 설계하였다. 크레인 시스템을 통한 시뮬레이션 분석에서 다른 기존의 제어기들보다 우수한 제어 수행을 증명하였다.

• 한국항만학회지
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• 제14권2호
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• pp.187-197
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied to the driving control of the autonomous guided vehicle(AGV). When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged by the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined through this off-line manner, these parameters are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted more accurately through the on-line fine tuning. The experiment for the control of steering and speed of AGV is performed. The results show that the proposed controller provides better performances than other conventional controllers.