[1] 张平,潘保芝,张莹,王鹏,董瑞新. 自组织神经网络在火成岩岩性识别中的应用[J]. 石油物探,2009, 48(1):53-56. Ping Z, Pan B Z, Zhang Y, Wang P, Dong R X. Application of self-organization maps network in identifying the lithology of igneous rock[J]. Geophysical Prospecting for Petroleum, 2009, 48(1):53-56. (in Chinese) [2] 陈蓉,王峰. 基于MATLAB的BP神经网络在储层物性预测中的应用[J]. 测井技术,2009, 33(1):75-78. Chen R, Wang F. Application of MATLAB-based of BP neural network in reservoir parameters prediction[J]. Well Logging Technology, 2009, 33(1):75-78. (in Chinese) [3] Hamidi H, Rafati R. Prediction of oil reservoir porosity based on BP-ANN[C]//IEEE International Conference on Innovation Management and Technology Research, 2012:241-246. [4] 肖波,韩学辉,周开金,支乐菲. 测井曲线自动分层方法回顾与展望[J]. 地球物理学进展,2010, 25(5):1802-1810. Xiao B, Han X H, Zhou K J, Zhi L F. A review and outlook of automatic zonation methods of well log[J]. Progress in Geophysics, 2010, 25(5):1802-1810. (in Chinese) [5] 靳玉萍,李保霖. 基于遗传优化径向基概率神经网络的岩性识别应用[J]. 计算机应用,2013, 33(2):353-356. Jin Y P, Li B L. Lithology identification based on genetic optimized radial basis probabilistic neural network[J]. Journal of Computer Applications, 2013, 33(2):353-356. (in Chinese) [6] 胡启华,范晶晶,张新. 应用BP神经网络预测油页岩含油率[J]. 计算机应用,2014(S2):186-189. Hu Q H, Fan J J, Zhang X. Application of BP neural network in oil content prediction[J]. Journal of Computer Applications, 2014(S2):186-189. (in Chinese) [7] 潘少伟,梁鸿军,李良,王家华. 改进PSO-BP神经网络对储层参数的动态预测研究[J]. 计算机工程与应用,2014, 50(10):52-56. Pan S W, Liang H J, Liang L, Wang J H. Dynamic prediction on reservoir parameter by improved PSO-BP neural network[J]. Computer Engineering & Applications, 2014, 50(10):52-56. (in Chinese) [8] 单敬福,陈欣欣,赵忠军,葛雪,张芸. 利用BP神经网络法对致密砂岩气藏储集层复杂岩性的识别[J]. 地球物理学进展,2015(3):1257-1263.) Shan J F, Chen X X, Zhao Z J, Ge X, Zhang Y. Identification of complex lithology for tight sandstone gas reservoirs SASE on BP neural net[J]. Progress in Geophysics, 30(3):1257-1263. doi:10.6038/pg20150335. (in Chinese) [9] 瞿晓婷,张蕾,冯宏伟,王惠亚,张涛,冯筠. 面向复杂储层的非均衡测井数据的岩性识别[J]. 地球物理学进展,2016, 31(5):2128-2132. Qu X T, Zhang L, Feng H W, Wang H Y, Zhang T, Feng Y. Lithology identification for imbalanced logging data on complex reservoirs[J]. Progress in Geophysics, 2016, 31(5):2128-2132, doi:10.6038/pg20160533. (in Chinese) [10] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554. [11] 吕启,窦勇,牛新,徐佳庆,夏飞. 基于DBN模型的遥感图像分类[J]. 计算机研究与发展,2014, 51(9):1911-1918. Lü Q, Dou Y, Niu X, Xu J Q, Xia F. Remote sensing image classification based on DBN model[J]. Journal of Computer Research & Development, 2014, 51(9):1911-1918. (in Chinese) [12] 孙劲光,蒋金叶,孟祥福,李秀娟. 一种数值属性的深度置信网络分类方法[J]. 计算机工程与应用,2014, 50(2):112-115. Sun J G, Jiang J Y, Meng X F, Li X J. DBN classification algorithm for numerical attribute[J]. Computer Engineering and Applications, 2014, 50(2):112-115. (in Chinese) [13] 孙劲光,蒋金叶,孟祥福,李秀娟. 深度置信网络在垃圾邮件过滤中的应用[J]. 计算机应用,2014, 34(4):1122-1125. Xun J G, Jiang J X, Meng X F, Li X J. Application of deep belief nets in spam filtering[J]. Journal of Computer Applications, 2014, 34(4):1122-1125. (in Chinese) [14] 樊雅琴,王炳皓,王伟,唐烨伟. 深度学习国内研究综述[J]. 中国远程教育,2015(6):27-33. Fan Y Q, Wang B H, Wang W, Tang Y W. A review of Chinese literature on deep learning[J]. Distance Education in China, 2015(6):27-33. (in Chinese) [15] 焦李成,杨淑媛,刘芳,王士刚,冯志玺. 神经网络七十年:回顾与展望[J]. 计算机学报,2016, 39(8):1697-1716. Jiao L C, Yang S Y, Liu F, Wang S G, Feng Z X. Seventy years beyond neural networks:retrospect and prospect[J]. Chinese Journal of Computers, 2016, 39(8):1697-1716. (in Chinese) |