지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.1
- 수록면
- 845 - 860 (16page)
이용수
초록· 키워드
오류제보하기
It is very important to study displacement prediction in geotechnical engineering. Nowadays, the grey system method, time series analysis method and artificial neural network method are three main methods. Based on the brief introduction, the three methods are analyzed comprehensively. Their merits and demerits, applied ranges are revealed. To solve the shortcomings of the artificial neural network method, a new prediction method based on new evolutionary neural network is proposed. Finally, through two real engineering applications, the analysis of three main methods and the new evolutionary neural network method all have been verified. The results show that, the grey system method is a kind of exponential approximation to displacement sequence, and time series analysis is linear autoregression approximation, while artificial neural network is nonlinear autoregression approximation. Thus, the grey system method can suitably analyze the sequence, which has the exponential law, the time series method can suitably analyze the random sequence and the neural network method almostly can be applied in any sequences. Moreover, the prediction results of new evolutionary neural network method is the best, and its approximation sequence and the generalization prediction sequence are all coincided with the real displacement sequence well. Thus, the new evolutionary neural network method is an acceptable method to predict the measurement displacements of geotechnical engineering.
목차
등록된 정보가 없습니다.
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
학교 실내 PM2.5 농도 예측모델 개발
대한건축학회 학술발표대회 논문집
2021 .04
-
Artificial Neural Network with Complex Structures
한국통신학회 학술대회논문집
2020 .08
-
A Prediction of the Uzbekistan’s Population Growth using Artificial Neural Networks
Proceedings of KIIT Conference
2018 .06
-
Artificial neural network calculations for a receding contact problem
Computers and Concrete, An International Journal
2020 .01
-
Deep Neural Network 를 이용한 음성 품질 평가에 관한 연구
한국통신학회 학술대회논문집
2017 .06
-
Study of the lower limb joint prediction neural network for tracking control of a mobile x-ray imaging system
대한기계학회 춘추학술대회
2021 .04
-
Intelligent optimal grey evolutionary algorithm for structural control and analysis
Smart Structures and Systems, An International Journal
2024 .05
-
Comparing Deep Learning Techniques for Modeling Photovoltaic Power Output
JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE
2017 .12
-
인공신경망을 이용한 실시간 보행상태 분류 알고리즘
한국생산제조학회 학술발표대회 논문집
2021 .12
-
인공 신경망(Artificial neural network)을 이용한 대장암 재발 예측
대한전자공학회 학술대회
2017 .11
-
Performance Prediction of Open Rotor Enabled by Neural Network
제어로봇시스템학회 국제학술대회 논문집
2023 .10
-
통신 실패에 강인한 분산 뉴럴 네트워크 분할 및 추론 정확도 개선 기법
대한임베디드공학회논문지
2021 .01
-
The hybrid uncertain neural network method for mechanical reliability analysis
International Journal of Aeronautical and Space Sciences
2015 .12
-
Artificial Neural Network-based Method for Stereoscopic 3D Reconstruction
제어로봇시스템학회 논문지
2020 .03
-
Technology and Practice in Geotechnical Engineering
대한토목학회지
2021 .08
-
In Situ Testing Methods in Geotechnical Engineering
한국지반신소재학회지
2021 .07
-
Data-driven fault detection and isolation of system with only state measurements and control inputs using neural networks
제어로봇시스템학회 국제학술대회 논문집
2021 .10
-
Self-Normalizing Neural Network 를 이용한 동작 패턴 인식의 향상
한국정밀공학회 학술발표대회 논문집
2018 .05
-
Prediction of Korean electricity consumption using sequential neural networks
대한환경공학회 학술발표논문집
2020 .11
-
Deep Neural Network 기반의 최적 조류 계산 연구
대한전기학회 학술대회 논문집
2022 .07
이 논문의 저자 정보
최근 본 자료
댓글(0)
0