한국해양대학교

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神經回路網을 이용한 知能形 制御시스템의 制御機 設計 및 具現에 관한 硏究

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dc.contributor.author 文熙根著 -
dc.date.accessioned 2017-02-22T06:28:38Z -
dc.date.available 2017-02-22T06:28:38Z -
dc.date.issued 2003 -
dc.date.submitted 56797-10-27 -
dc.identifier.uri http://kmou.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002173930 ko_KR
dc.identifier.uri http://repository.kmou.ac.kr/handle/2014.oak/9606 -
dc.description.abstract An artificial neural network is an information-processing system that has certain performance characteristics in common with biological neural networks. Artificial neural networks have been developed as generalizations of mathematical models of human cognition or neural biology, based on the assumptions. In this study, this system makes use of the analog sensor and converts the feature of fish outline when sensor is operating with CPU(80C196KC). Then, after signal processing, this feature is classified a special feature and a outline of fish by using the neural network, one of the artificial intelligence scheme. This neural network classifies fish pattern of very simple and short calculation. This has linear activation function and the errror back propagation is used as a learning algorithm. And the neural network is learned in off-line process. Because an adaptation period of neural network is too long when random initial weights are used, off-line learning is induced to decrease the progress time An "Fillet machines" is a fillet extracting-tail cutting machine that is commonly used in the fish processing industry. Millions of dollars worth of "pollack" are wasted annually due to inaccurate fillet cutting using these somewhat outdated machines. The main cause of wastage is the "over-feed problem". This occurs when a pollack is inaccurately positioned with point to the cutter blade so that the cutting location is into fillet of a pollack. An effort has been made to correct this situation by sensing the position of the fillet using sensors accordingly. We confirmed this method has better performance than somewhat outdated machines. -
dc.description.tableofcontents 목차 Abstract = i 1. 서론 = 1 제2장 신경회로망 = 3 2.1 신경회로망 모델 = 4 2.2 다층 신경회로망의 구조와 학습 = 7 2.3 모멘텀 항 연산 = 12 3. 지능형 제어 시스템의 구성 = 14 3.1 센서회로부 = 14 3.2 마이크로 컨트롤러 시스템 = 15 3.3 DC 서보 모터 제어 = 16 4. 지능형 제어 시스템의 제어기 구현 및 실험 = 20 4.1 전체적인 시스템의 제어기 구현 및 패턴 분류 = 20 4.2 마이크로 컨트롤러를 사용한 모터 위치 제어 및 시퀀스 동작 = 27 4.3 실험 및 고찰 = 30 5. 결론 = 36 참고문헌 = 37 부록 = 39 -
dc.publisher 한국해양대학교 대학원 -
dc.title 神經回路網을 이용한 知能形 制御시스템의 制御機 設計 및 具現에 관한 硏究 -
dc.title.alternative A Study on the Controller Design and Implementation of Intelligent Control System using Neural Network -
dc.type Thesis -
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