• 한국지능시스템학회논문지
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• 제11권4호
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• pp.336-339
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• 2001

오존농도 메커니즘은 매우 복잡하고, 비선형성과 비정상성이 강하기 때문에 오존 예보시스템들은 많은 문제점을 가지고 있다. 특히 고농도 오존에 있어서 예측결과들이 성능이 좋지 않다. 본 논문은 뉴로-퍼지기법과 퍼지 클러스터링을 이용한 오존 예측시스템의 모델링 방법을 설명하고자 한다. GMDH의 전형적인 알고리즘에 기초한 동적 다항식 신경망은 데이터 분석, 비선형적이고 복잡한 시스템의 검증 그리고 동적 시스템의 예측을 위한 유용한 방법이다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1101-1106
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• 2005

The purpose of this study was to develop a process management system to manage ingot fabrication and the quality of the ingot. The ingot is the first manufactured material of wafers. Operating data (trace parameters) were collected on-line but quality data (measurement parameters) were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Thus, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were employed for data generation, and then modeling was accomplished, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to the control parameters. The dynamic polynomial neural network (DPNN) was used for data modeling that used the ingot fabrication data.

• International Journal of Control, Automation, and Systems
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• 제4권3호
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• pp.372-381
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• 2006

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.