http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : 이성칠 ( Yi Sung-chil ) (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
조효남 ( Cho Hyo-nam ),이성칠 ( Yi Sung-chil ),허춘근 ( Hur Choon-kun ) 한국구조물진단유지관리공학회 2001 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.5 No.2
This paper presents an efficient algorithm for the estimation of damage location and severity in structure using Probabilistic Neural Network (PNN). Artificial neural network has been being used for damage assessment by many researchers, but there are still some barriers that must be overcome to improve its accuracy and efficiency. The major problems with the conventional neural network are the necessity of many training data for neural network learning and ambiguity in the relation of neural network structure to the convergence of solution. In this paper, PNN is used as a pattern classifier to overcome those problems in the conventional neural network. The basic idea of damage assessment algorithm proposed in this paper is that modal characteristics from a damaged structure are compared with the training patterns which represent the damage in specific element to determine how close it is to training patterns in terms of the probability from PNN. The training pattern that gives a maximum probability implies that the element used in producing the training pattern is considered as a damaged one. The proposed damage assessment algorithm using PNN is applied to a Cable Stayed Bridge to verify the algorithm.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
