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
- 저널정보
- 한국콘크리트학회 International Journal of Concrete Structures and Materials International Journal of Concrete Structures and Materials Vol.13 No.2
- 발행연도
- 2019.2
- 수록면
- 295 - 307 (13page)
이용수
초록· 키워드
오류제보하기
In this study, an output-based neuro controller was built based on the idea of the adaptive neuro-fuzzy inference system (ANFIS) and its capabilities in response simulation, data cleansing and restoration capability were verified using measurement data from actual structural testing. The ANFIS is a family of the deep learning algorithm, which incorporates the benefits of adaptive control technique, artificial neural network, and the fuzzy inference system. Thus, it is expected to produce very accurate predictions even for the highly nonlinear system. Forced vibration responses of a five-story steel building were simulated by ANFIS and its accuracy was compared with the results of Recurrent Neural Network (RNN), which is a type of traditional artificial neural networks. Simulations by ANFIS were very accurate with a much lower root means square error (RMSE) than RNN. Simulated data by ANFIS showed an almost perfect match with the original. Even the small ripples in the power spectrum plot outside the dominant frequency were successfully reproduced. In addition, the ANFIS was used to increase the sampling rate of dynamic data. It was shown that missing high-frequency contents could be successfully reproduced when the ANFIS was properly trained. Lastly, The ANFIS was applied to remove the noise in the measured data from RC column cyclic load tests. The outliers were corrected effectively, but the tendency of flattening the peak values was observed.
목차
Abstract
1 Introduction
2 Deep Learning Algorithm
3 Performance Comparison of RNN and ANFIS
4 Applications of ANFIS
5 Conclusion
References
참고문헌 (29)
참고문헌 신청
Bak, C., & Son, H. (2017). Development of prediction model for root industry production process using artificial neural network. Journal of the Korean Society for Precision Engineering, 34(1), 23?27.
Barbounis, T. G., Theocharis, J. B., Alexiadis, M. C., & Dokopoulos, P. S. (2006). Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Transactions on Energy Conversion., 21(1), 273?284.
Becker, S., & Le Cun, Y. (1988) Improving the convergence of back-propagation learning with second order methods. In Proceedings of the 1988 Connectionist Models Summer School (pp. 29?37). San Matteo: Morgan Kaufmann.
Brown, M., & Harris, C. J. (1994). Neurofuzzy adaptive modelling and control. Upper Saddle River: Prentice Hall.
Fahlman S. E. (1988). An empirical study of learning speed in back-propagation networks.
Barbounis, T. G., Theocharis, J. B., Alexiadis, M. C., & Dokopoulos, P. S. (2006). Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Transactions on Energy Conversion., 21(1), 273?284.
Becker, S., & Le Cun, Y. (1988) Improving the convergence of back-propagation learning with second order methods. In Proceedings of the 1988 Connectionist Models Summer School (pp. 29?37). San Matteo: Morgan Kaufmann.
Brown, M., & Harris, C. J. (1994). Neurofuzzy adaptive modelling and control. Upper Saddle River: Prentice Hall.
Fahlman S. E. (1988). An empirical study of learning speed in back-propagation networks.
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