深度学习神经网络在力学模型参数估计中的应用研究进展
Some Developments of Estimation Procedures of Mechanical Model Parameter Based on Deep Learning Neural Network
摘要: 为了估计岩土材料的力学模型参数，讨论了基于经典BP神经网络的参数反演方法的基本框架和算法。分析了经典BP神经网络所存在的某些缺陷及其改进方法。结合两个基于深度学习神经网络估计锂离子电池电化学模型参数的例子，介绍了基于深度学习神经网络估计模型参数的基本思路。讨论了深度学习神经网络超参数确定方法，分析了提高深度学习神经网络学习效率和泛化能力的某些行之有效的策略。
Abstract: In order to estimate mechanical model parameters of materials, the basic scheme and algorithm of parameter inversion based on classical BP neural network are discussed. Some drawbacks and improvement methods for classical BP neural network are analyzed. Combined two examples of estimating model parameters of electric-chemical model of Li-ion cells, the basic thinking of esti-mating model parameter procedure based on deep learning neural network is introduced. How to determine hyper parameters of deep learning neural network is discussed. How to improve learning efficiency and generalization ability of deep learning neural network is developed to es-timate model parameters of materials.
文章引用：李守巨. 深度学习神经网络在力学模型参数估计中的应用研究进展[J]. 人工智能与机器人研究, 2020, 9(2): 100-109. https://doi.org/10.12677/AIRR.2020.92012

参考文献

[1] Albert Tarantla. 模型参数估计的反问题理论与方法[M]. 北京: 科学出版社, 2009.
[2] Li, S.-J. and Shao, L.-T. (2012) Inverse Procedure for Determining Model Parameter of Soils Using Real-Coded Genetic Algorithm. Journal of Central South University, 19, 1764-1770. [Google Scholar] [CrossRef] 
[3] 李守巨, 于申, 孙振祥. 基于神经网络的堆石料本构模型参数反演[J]. 计算机工程, 2014, 46(4): 267-271.
[4] Lecun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444. [Google Scholar] [CrossRef] [PubMed]
[5] 山下隆义. 图解深度学习[M]. 北京: 中国工信出版社, 2018.
[6] 涌井良幸. 深度学习的数学[M]. 北京: 中国工信出版社, 2019.
[7] 斋藤康毅. 深度学习入门[M]. 北京: 中国工信出版社, 2018.
[8] Chun, H.Y. (2019) Parameter Identification of an Electrochemical Lithium-Ion Battery Model with Conventional Neural Network. IFAC Paperonline, 52, 129-134. [Google Scholar] [CrossRef] 
[9] Shen, S. (2019) A Deep Learning Method for Online Capacity Estimation of Lithium-Ion Batteries. Journal of Energy Storage, 25, 1-13.
[10] Kaba, K. (2018) Estimation of Daily Global Solar Radiation Using Deep Learning Model. Energy, 162, 126-135. [Google Scholar] [CrossRef] 
[11] Radoslaw, M.C. and Aniel, K. (2019) Deep Neural Networks as Scientific Models. Trends in Cognitive Sciences, 23, 305-317.
[12] Taesic, L., Juwon, K., Young, U. and Hyunju, L. (2019) Deep Neural Network for Estimating Low Density Lipoprotein Cholesterol. Clinica Chimica Acta, 489, 35-40. [Google Scholar] [CrossRef] [PubMed]
[13] Cui, M.J. and Khodayar, M. (2019) Deep Learning Based Time-Varying Parameter Identification for System-Wide Load Modeling. IEEE Transactions on Smart Grid, 10, 6102-6114. [Google Scholar] [CrossRef] 
[14] Weimer, D. and Scholz-Reiter, B. (2016) Design of Deep Convolutional Neural Network Architectures for Automated Feature Extraction in Industrial Inspection. CIRP Annals-Manufacturing Technology, 65, 417-420. [Google Scholar] [CrossRef] 
[15] Yu, H.Y. and Khan, F. (2015) Nonlinear Gaussian Belief Network Based Fault Diagnosis for Industrial Processes. Journal of Process Control, 35, 178-200. [Google Scholar] [CrossRef] 
[16] 金列俊, 詹建明, 陈俊华. 基于一维卷积神经网络的钻杆故障诊断[J]. 浙江大学学报: 工学版, 2020, 54(3): 1-8.
[17] 朱锡祥, 刘凤山. 基于一维卷积神经网络的车载语音识别研究[J]. 微电子学与计算机, 2017, 34(11): 21-25.
[18] 韩林洁, 石春鹏, 张建超. 基于一维卷积神经网络的轴承剩余寿命预测[J]. 制造自动化, 2020, 42(3): 10-13.
[19] LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998) Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86, 2278-2324. [Google Scholar] [CrossRef] 
[20] Srivastava, N. and Hinton, G. (2014) Dropout: A Simple Way to Prevent Neural Networks from Overfitting. The Journal of Machine Learning Research, 15, 1929-1958.
[21] Wang, L., Scott, K.A., Xu, L. and Clausi, D.A. (2016) Sea Ice Concentration Estimation during Melt from Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study. IEEE Transactions on Geoscience and Remote Sensing, 54, 4524-4533. [Google Scholar] [CrossRef] 
[22] Vedaldi, A. and Lenc, K. (2015) Matconvnet: Convolutional Neural Networks for MATLANB. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia, 26-30 October 2015, 689-692. [Google Scholar] [CrossRef] 
[23] Ahmad Radzi, S. (2016) A MATLAB-based Convolutional Neural Network Approach for Face Recognition System. Journal of Bioinformatics and Proteomics Review, 2, 1-5. [Google Scholar] [CrossRef] 

为你推荐