Citation: | QIU Dongwei, WANG Tong, DUAN Mingxu, LUO Dean, WANG Laiyang. Construct and Application of LM-CDBN Deformation Prediction Model for Supertall Buildings[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 310-316. doi: 10.3969/j.issn.0258-2724.20180293 |
邓勇,张冠宇,李宗春,等. 遗传小波神经网络在变形预报中的应用[J]. 测绘科学,2012,37(5): 183-186.
DENG Yong, ZHANG Guanyu, LI Zongchun, et al. Application of genetic wavelet neural network in deformation forecast[J]. Science of Surveying and Mapping, 2012, 37(5): 183-186.
|
段明旭,邱冬炜,李婉,等. 改进灰色人工神经网络模型的超高层建筑变形预测[J]. 测绘科学,2017,42(4): 141-146.
DUAN Mingxu, QIU Dongwei, LI Wan, et al. The GM-BPNN prediction research in the deformation forecasting of the super high-rise building[J]. Science of Surveying and Mapping, 2017, 42(4): 141-146.
|
WANG Xiaoyu, YANG Kan, SHEN Changsong. Study on MPGA-BP of gravity dam deformation prediction[J]. Mathematical Problems in Engineering, 2017, 2017(6): 1-13.
|
HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. doi: 10.1126/science.1127647
|
CHEN H, MURRAYA F. Continuous restricted Boltzmann machine with an implementable training algorithm[J]. IEE Proceedings—Vision,Image and Signal Processing, 2003, 150(3): 153-158. doi: 10.1049/ip-vis:20030362
|
周晓莉,张丰,杜震洪,等. 基于CRBM算法的时间序列预测模型研究[J]. 浙江大学学报(理学版),2016,43(4): 442-451. doi: 10.3785/j.issn.1008-9497.2016.04.011
ZHOU Xiaoli, ZHANG Feng, DU Zhenhong, et al.A study on time series prediction model based on CRBM algorithm[J]. Journal of Zhejiang University (Science Edition), 2016, 43(4): 442-451. doi: 10.3785/j.issn.1008-9497.2016.04.011
|
HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7): 1527-1554. doi: 10.1162/neco.2006.18.7.1527
|
CHEN Siqi, AMMAR H B, TUYLS K, et al. Conditional restricted Boltzmann machines for negotiations in highly competitive and complex domains[C]//International Joint Conference on Artificial Intelligence. Beijing: AAAI, 2013: 69-75.
|
SPILIOPOULOU A. Investigation of deep CRBM networks in modeling sequential data[D]. Edinburgh: University of Edinburgh, 2008.
|
MNIH V L H, HINTON G E. Conditional restricted Boltzmann machines for structured output prediction[C]//Twenty-Seventh Conference on Uncertainty in Artificial Intelligence. Barcelona: AUAI Press, 2011: 514-522.
|
周晓莉. 基于深度学习的浙江近岸船舶数据赤潮生物量趋势性预测研究[D]. 杭州: 浙江大学, 2016.
|
MADSEN K, NIELSEN H B, TINGLEFF O. Methods for nonlinear least squares problems[J]. Society for Industrial & Applied Mathematics, 2004, 2012(1): 1409-1415.
|
QUESADA-OLMO N, JIMENEZ-MATINEZ M J, FARJAS-ABADIAM. Real-time high-rise building monitoring system using global navigation satellite system technology[J]. Measurement, 2018, 123(1): 115-124.
|
LECUN Y, BENGIO Y, HINTON G E. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
|
GAO Jun. Newton-Gauss curvature matrix based cdbnfor online edible fungus drying prediction model[J]. Future Generation Computer Systems, 2017, 81(1): 273-279.
|
HRASKO R, PACHECO A G C, KROHLING RA. Time series prediction using restricted Boltzmann machines and backpropagation[C]//Information Technology and Quantitative Management. Suzhou: Elsevier, 2015: 990-999.
|
BA J, HINTON G E, MNITH V, et al. Using fast weights to attend to the recent past[C]//Advances in Neural Information Processing Systems. Barcelona: Elsevier, 2016: 4331-4339.
|
SRIVASTAVA N, SALAKHUTDINOV R, HINTON G E. Modeling documents with a deep Boltzmann machine[C]//Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence. Bellevue: AUAI press, 2013: 616-624.
|