AUTOMATIC CONTROL OF THE TECHNOLOGICAL PROCESS USING NEURAL NETWORKS TO DETERMINE THE PARAMETERS OF THE PRODUCTION PROCESS
DOI:
https://doi.org/10.33042/2522-1809-2022-3-170-7-11Keywords:
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.
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
Downloads
Published
How to Cite
Issue
Section
License
The authors who publish in this collection agree with the following terms:
• The authors reserve the right to authorship of their work and give the magazine the right to first publish this work under the terms of license CC BY-NC-ND 4.0 (with the Designation of Authorship - Non-Commercial - Without Derivatives 4.0 International), which allows others to freely distribute the published work with a mandatory reference to the authors of the original work and the first publication of the work in this magazine.
• Authors have the right to make independent extra-exclusive work agreements in the form in which they were published by this magazine (for example, posting work in an electronic repository of an institution or publishing as part of a monograph), provided that the link to the first publication of the work in this journal is maintained. .
• Journal policy allows and encourages the publication of manuscripts on the Internet (for example, in institutions' repositories or on personal websites), both before the publication of this manuscript and during its editorial work, as it contributes to the emergence of productive scientific discussion and positively affects the efficiency and dynamics of the citation of the published work (see The Effect of Open Access).
