基于粒子群算法的深度置信网络焙烧过程可溶锌率预测
Prediction of Soluble Zinc Rate during Roasting Process Based on Particle Swarm Optimization in Deep Belief Network

DOI: 10.12677/CSA.2020.101016, PP. 141-153

Keywords: 深度置信网络，网络结构，信息熵，粒子群算法，可溶锌率
Deep Belief Network, Information Entropy, Network Structure, Particle Swarm Optimization, Soluble Zinc Rate

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对锌冶炼焙烧过程焙砂质量衡量指标可溶锌率难以在线测量的问题，提出采用深度置信网络算法(Deep Belief Network, DBN)预测可溶锌率。但DBN网络结构是影响其预测性能的重要因素，合适的网路结构又难以确定，故提出先用信息熵法确定合适的隐层层数，再用PSO算法对隐层节点数和学习率进行优化，最终确定DBN网路结构。通过数据集仿真和可溶锌率预测实际应用，对该方法进行验证，与BP神经网络、RBF神经网络预测模型进行对比。结果表明，用信息熵法和PSO算法优化确定的DBN网络结构，预测精度更高，拟合能力更强。
In order to solve the problem that the soluble zinc ratio is difficult to be measured online in the roasting quality of zinc smelting and roasting process, a Deep Belief Network (DBN) algorithm was proposed to predict the soluble zinc rate. However, the DBN network structure is an important factor affecting its prediction performance, and it is difficult to determine the appropriate network structure. It is proposed to use the information entropy method to determine the appropriate number of hidden layer, then use the PSO algorithm to optimize the number of hidden layer nodes and learning rate, and finally determine the DBN network structure. The method was validated by data set simulation and practical application of soluble zinc rate prediction, and compared with BP neural network and RBF neural network models. The results show that the DBN network structure optimized by the information entropy method and the PSO algorithm has higher prediction accuracy and stronger fitting ability.

References

[1]	Lee, T.S. and Mumford, D. (2003) Hierarchical Bayesian Inference in the Visual Cortex. Journal of the Optical Society of America, 20, 1434-1448.
[2]	潘广源, 柴伟, 乔俊飞. DBN网络的深度确定方法[J]. 控制与决策, 2015, 30(2): 256-260.
[3]	Hinton, G.E. and Salakhutdinov, R.R. (2006) Reducing the Dimensionality of Data with Neural Net-works. Science, 313, 504-507. https://doi.org/10.1126/science.1127647
[4]	Dahl, G.E., Yu, D., Deng, L. and Ac-ero, A. (2011) Large Vocabulary Continuous Speech Recognition with Context-Dependent DBN-HMMS. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, Prague Congress Center, Prague, 22-27 May 2011, 4688-4691. https://doi.org/10.1109/ICASSP.2011.5947401
[5]	徐春华, 陈克绪, 马建, 刘佳翰, 吴建华. 基于深度置信网络的电力负荷识别[J]. 电工技术学报, 2019, 34(19): 4135-4142.
[6]	毛勇华, 代兆胜, 桂小林. 一种改进的5层深度学习结构与优化方法[J]. 计算机工程, 2018, 44(6): 147-150.
[7]	Wang, Y.B., You, Z.H., Li, X., et al. (2017) Predicting Protein-Protein Interactions from Protein Sequences by a Stacked Sparse Autoencoder Deep Neural Network. Molecular Biosystems, 13, 1336-1344. https://doi.org/10.1039/C7MB00188F
[8]	Liu, F., Jiao, L.C., Hou, B. and Yang, S.Y. (2016) POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information. IEEE Transactions on Geoscience & Remote Sens-ing, 54, 1-17. https://doi.org/10.1109/TGRS.2016.2514504
[9]	Kong, W., Zhao, Y.D., Hill, D.J., Luo, F. and Xu, Y. (2018) Short-Term Residential Load Forecasting Based on Resident Behaviour Learning. IEEE Transactions on Power Systems, 33, 1087-1088. https://doi.org/10.1109/TPWRS.2017.2688178
[10]	Li, B.-Q., He, Y.-Y., Guo, Y.-S. and Qiu, Y. (2017) Auto-matic Interpretation Algorithm for Tunnel Geological Prediction Based on DBN. Journal of Chang’an University (Natu-ral Science Edition), 37, 90-96.
[11]	高月, 宿翀, 李宏光. 一类基于非线性PCA和深度置信网络的混合分类器及其在PM2.5浓度预测和影响因素诊断中的应用[J]. 自动化学报, 2018, 44(2): 318-329.
[12]	Zhou, S.Z.S., Chen, Q.C.Q. and Wang, X.W.X. (2010) Discriminative Deep Belief Networks for Image Classification. 2010 IEEE Interna-tional Conference on Image Processing, Hong Kong, 26-29 September 2010, 1561-1564. https://doi.org/10.1109/ICIP.2010.5649922
[13]	张媛媛, 霍静, 杨婉琪, 等. 深度信念网络的二代身份证异构人脸核实算法[J]. 智能系统学报, 2015, 10(2): 193-200.
[14]	朱乔木, 党杰, 陈金富, 徐友平, 李银红, 段献忠. 基于深度置信网络的电力系统暂态稳定评估方法[J]. 中国电机工程学报, 2018, 38(3): 735-743.
[15]	张楠, 丁世飞, 张健, 赵星宇. 基于噪声数据与干净数据的深度置信网络[J]. 软件学报, 2019, 30(11): 3326-3339.
[16]	Lv, Y., Duan, Y., Kang, W., Li, Z. and Wang, F.-Y. (2015) Traffic Flow Prediction With Big Data: A Deep Learning Ap-proach. IEEE Transactions on Intelligent Transportation Systems, 16, 865-873.
[17]	许冬. 复杂锌精矿沸腾焙烧预测神经网络研究[D]: [硕士学位论文]. 西安: 西安建筑科技大学, 2008.
[18]	Shen, F., Chao, J. and Zhao, J. (2015) Forecasting Exchange Rate Using Deep Belief Networks and Conjugate Gradient Method. Neurocomputing, 167, 243-253. https://doi.org/10.1016/j.neucom.2015.04.071
[19]	张国辉. 基于深度置信网络的时间序列预测方法及其应用研究[D]: [硕士学位论文]. 哈尔滨: 哈尔滨工业大学, 2017.

Full-Text