参考文献/References:
参考文献 References
[1] NOVSELOW K. S., GEIM A. K., MOROZOW S. V., et
al. Electric field effect in atomically thin carbon films[J].
Science, 2004, (306):666-669.
[2] CORDELIA Sealy. Graphene goes from strength to
strength[J]. Materials Today, 2008, 11(9):12-18.
[3] LI Peiyuan, XIE Zhijiang, LI Xinxia. Research into fault
diagnosis of large rotating machinery on BP network and
the data source of network[J]. Journal of Southwest
University for Nationalities Natural Science Edition,
2004, 30(3):386-390.
[4] SHOKRI S H M, SHOKRI E H, ROHAM Raffiee.
Prediction of Young’s modulus of graphene sheets and
carbon nanotubes using nanoscale continuum mechanics
approach[J]. Materials and Design, 2010, 31(2):790-795.
[5] 余晓红.BP 神经网络的MATLAB 编程实现及讨论[J].
浙江交通职业技术学院学报, 2007, 8(4):45-48.
YU Xiaohong. Matlab implementation and discussion of
BP neural network[J]. Journal of Zhejiang Institute of
Communications, 2007, 8(4):45-48.
[6] Venkatesh. Predicting the mechanical characteristics of
hydrogen functionalied graphene sheets using artificial
netural network approach[J]. Journal Of Nanostructure in
Chemistry, 2013, (3):83-87.
[7] 尹海莲,胡自力. 基于BP 神经网络的复合材料性能
预测[J].南京航空航天大学学报, 2006, 38(2):234-238.
YI Hailian, HU Zili. Prediction of composite material
properties basedon bp algorithm of artificial neutral
etwork[J].Journal of Nanjing University of Aeronautics
& Astronautics, 2006, 38(2):234-238.
[8] 白光辉,孟鹤松,杜善文,等.基于神经网络炭/炭复
合材料烧蚀性能预测[J].复合材料学报, 2007, 26(4):
83-88.
BAI Guanghui, MENG HeSong, DU Shanwen, et al.
Prediction on the ablative performance of carbon/carbon
composites based on artificial neutral network[J]. Acta
Materie Compositae Sinica, 2007, 26(4):83-88.
[9] 李东波.基于ANN 的碳纤维楠竹锚杆锚固力预测研究
[J].力学与实践, 2013, 35(2):40-45.
LI Dongbo. Anchorage force prediction for the
cfrp-bamboo bolt based on artificial neural network[J].
Mechanics in Engineering, 2013, 35(2):40-45.
[10] 王伟.人工神经网络入门与应用[M].北京:北京航空航
天大学,1995.
WANG wei. The introduction and application of artificial
neural network[M]. Beijing: Beijing University of Aeronautics
and Astronautics Press, 1995.
[11] Tho K. K., SWADDIWUDHIPONG S, LIU Z S, et al.
Artificial neural network model for material characterization
by indentation[J]. Modelling and Simul. Mater.
Sci. Eng, 2004, 12(5):1055-1062.
[12] 朱熹育,王社良,朱军强.基于Sugeno 型模糊神经网
络的空间杆系结构的压电驱动器主动控制[J].工程力
学, 2013, 30(8):272-277.
ZHU Xiyu, WANG Sheliang, ZHU Junqiang. Sugeno
type fuzzy neural network active cortrol of space frame
structure based on piezoelectric actuator[J]. Engineering
Mechanics, 2013, 30(8):272-277.
[13] 沈乐.石墨烯薄膜的等效弹性参数和力学行为研究[D].
上海:上海交通大学大学,2010.
SHEN Le. Effective elastic properties and mechanical
behavior of single layer graphene sheets[D].Shanghai:
Shanghai Jiao Tong University, 2010.
[14] XU Yumou, SHEN Huishen, ZHANG Chenli. Nonlocal
plate model for nonlinear bending of bilayer graphene
sheets subjected to transverse loads in thermal
environments[J]. Composite Structures, 2013, 98(9):
294-302.
[15] 韩同伟,贺同飞,王健,等.石墨烯拉伸力学性能温度
相关性的数值模拟[J].同济大学学报, 2009, 37(12):
1638-1641.
HAN Tongwei, HE Pengfei, WANG Jian, et al.
Numerical simulation of temperature dependence of
tensile mechanical properties for single graphene sheet[J].
Journal of Tongji University, 2009, 37(12):1638-1641.
[16] 韩同伟,贺鹏飞,王健,等.单层石墨烯薄膜拉伸变
形的分子动力学模拟[J]. 新型炭材料, 2010,
25(4):261-266.
HAN Tongwei, HE Pengfei, WANG Jian,et al. Molecular
dynamics simulation of a single graphene sheet under
tension[J]. New Carbon Materials, 2010, 25(4):261-266.
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