AUTOMATIC CONTROL OF THE TECHNOLOGICAL PROCESS USING NEURAL NETWORKS TO DETERMINE THE PARAMETERS OF THE PRODUCTION PROCESS

Authors

  • R. Voronov O.M. Beketov National University of Urban Economy in Kharkiv
  • O. Donets O.M. Beketov National University of Urban Economy in Kharkiv

DOI:

https://doi.org/10.33042/2522-1809-2022-3-170-7-11

Keywords:

automatic control, oil and gas industry, technological process, computer networks, deep neural networks.

Abstract

In multifactorial systems using textual and graphical information in matrix factorization to facilitate the problem of separate data processing. Recently, in some studies, the study of neural networks to understand the content of text and graphic elements more deeply and to achieve efficacy by creating more accurate patterns of recognition of elements. However, the open question remains about how to effectively use graphic data from the thermal imager in matrix factorization. In this paper, we proposed a double-regularized matrix factorization with deep neural networks (DRMF) to solve this problem. DRMF applies a multilayered neural network model by stacking a convolutional neural network and a secured repetitive neural network to create independent distributed views of user content and objects. Then representations serve to regularize the generation of hidden models for both users and for elements of matrix factorization. So the proposed new model of the neural network works better than a model with a single convergent neural network.

In this paper, we propose double - regularized matrix factorization with deep neural networks (DRMF) to solve this problem. DRMF uses a multi-layered neural network model by enclosing a convoluted neural network and a secure repeating neural network to create independent distributed representations of user content and objects. Then the representations are used to regularize the generation of hidden models for both users and elements of matrix factorization. Thus, the proposed new neural network model works better than the model with a single converging neural network.

In traditional SF methods, only a feedback matrix is ​​used, which contains explicit (eg, estimates) or implicit feedback to train and predict the life of the motor. As a rule, the feedback matrix is ​​liquid, which means that most users encounter several elements. Based on this was presented in Proc. BigData Congress. However, this view has been significantly expanded using a new deep neural network model and adding new experimental attachments compared to the conference publication.

Author Biographies

R. Voronov, O.M. Beketov National University of Urban Economy in Kharkiv

PhD, Senior Lecturer of the Department

O. Donets, O.M. Beketov National University of Urban Economy in Kharkiv

PhD, Associate Professor of the Department

References

Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

Bobadilla, J., Ortega, F., Hernando, A., Gutierrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132. DOI: https://doi.org/10.1016/j.knosys.2013.03.012

Cao, D., He, X., Nie, L., Wei, X., Hu, X., Wu, S., Chua, T. (2017). Cross-platform app recommendation by jointly modeling ratings and texts. ACMTrans. Information Systems, 35, 1–37. DOI: https://doi.org/10.1145/3017429

Gao, J., Zhou, T. (2017). Evaluating user reputation in online rating systems via an iterative group-based ranking method. Physica: Statistical Mechanics Its Applications, 473, 546–560. DOI: https://doi.org/10.48550/arXiv.1509.00594

He, R., McAuley, J. (2016). Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. 25th International Conference on World Wide Web (WWW’16), 507–517. DOI: https://doi.org/10.48550/arXiv.1602.01585

Johnson, R., Zhang, T. (2014). Effective use of word order for text categorization with convolutional neural networks. 34th Annual Conference of IEEE Industrial Electronics, 268–273. DOI: https://doi.org/10.48550/arXiv.1412.1058

Koren, Y., Bell, R., Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Compute, 42, 30–37.

Lu, L., Zhou, T. (2011). Link prediction in complex networks. Physica: Statistical Mechanics and Its Applications, 390, 1150–1170. DOI: https://doi.org/10.48550/arXiv.1010.0725

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J. (2013). Distributed representations of words and phrases and their compositionality. The 27th Annual Conference on Neural Information Processing Systems (NIPS’13), 3111–3119. DOI: https://doi.org/10.48550/arXiv.1310.4546

Salakhutdinov, R., Mnih, A. (2007). Probabilistic matrix factorization. Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems (NIPS’07), 1257–1264.

Tang, D., Qin, B., Liu, T. (2015). Document modeling with gated re-current neural network for sentiment classification. Proceedings of the 25 Conference on Empirical Methods in Natural Language Processing (EMNLP’15), 1422–1432. DOI: http://dx.doi.org/10.18653/v1/D15-1167

Wang, C., Blei, D.M. (2011). Collaborative topic modeling for recommending scientific articles. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11), 448–456. DOI: https://doi.org/10.1145/2020408.2020480

Zhang, F., Yuan, N. J., Lian, D., Xie, X., Ma, W.-Y. (2016). Collaborative knowledge base embedding for recommender systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16), 353–362. DOI: https://doi.org/10.1145/2939672.2939673

Zhao, D.-D., Zeng, A., Shang, M.-S., Gao, J. (2013). Long-term effects of recommendation on the evolution of online systems. Chinese Physics Letters, 30, 888–901.

Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., Zhang, Y.-C. (2010). Solving the apparent diversity-accuracy dilemma of recommender systems. The Proceedings of the National Academy of Sciences of the United States of America (PNAS), 107, 4511–4515. DOI: https://doi.org/10.1073/pnas.1000488107

Published

2022-06-24

How to Cite

Voronov, R., & Donets, O. (2022). AUTOMATIC CONTROL OF THE TECHNOLOGICAL PROCESS USING NEURAL NETWORKS TO DETERMINE THE PARAMETERS OF THE PRODUCTION PROCESS. Municipal Economy of Cities, 3(170), 7–11. https://doi.org/10.33042/2522-1809-2022-3-170-7-11