A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models
| Назва | A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models |
| Назва англійською | A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models |
| Автори | Vasyl Martsenyuk, Nataliia Kit |
| Принадлежність | University of Bielsko-Biala, Bielsko-Biala, Poland Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine |
| Бібліографічний опис | A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models / Vasyl Martsenyuk, Nataliia Kit // Scientific Journal of TNTU. — Tern.: TNTU, 2024. — Vol 114. — No 2. — P. 39–50. |
| Bibliographic description: | Martsenyuk V., Kit N. (2024) A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models. Scientific Journal of TNTU (Tern.), vol 114, no 2, pp. 39–50. |
| DOI: | https://doi.org/10.33108/visnyk_tntu2024.02.039 |
| УДК | 004.8 |
| Ключові слова | Design of production networks, lattice model, qualitative analysis, multivariate forecasting method, multilayer neural models. |
Design of production network based on multilayer neural models is considered in this paper. Design of production network is crucial because it determines the optimal location of production and logistics facilities, affects cost efficiency, customer service level and overall competitiveness in the global market. Multi-layer neural networks play an important role in this process, using advanced algorithms, machine learning models and optimization techniques to analyze huge amounts of data. Special attention is focused on qualitative analysis of dynamic behavior, dynamic lattice model. The model includes rate constants and initial conditions affecting the model trajectories, which can be classified as a stable site, limit cycle, or chaotic attractor. We aim to solve the problem of qualitative behavior of the model as a problem of multilayer neural models. A multivariate method of predicting nonlinear dynamics was used to construct the training data set. Neural networks defined by regenerative architectures with linear and non-linear outputs were analyzed and compared. As a result of the analysis, it was found that architectures with linear outputs show better correspondence between expected and predicted values. Architectures with non-linear outputs, despite their complexity, exhibit less accuracy and more deviation compared to linear ones. The single-layer architecture with linear outputs shows the best accuracy, although the two-layer architecture with linear outputs has the lowest rms error. Architectures with non-linear outputs have faster training times but poor accuracy, while architectures with linear outputs require more training time but have lower errors. The results obtained in the work indicate the importance of choosing the right architecture of the neural network depending on the tasks and requirements for accuracy and training time of the model. | |
| ISSN: | 2522-4433 |
| Перелік літератури |
1. Jelena Milisavljevic-Syed, Janet K. Allen, Sesh Commuri, Farrokh Mistree, Design of networked manufacturing systems for Industry 4.0, Procedia CIRP, vol. 81, 2019, рр. 1016–1021. Available at: https://doi.org/10.1016/j.procir.2019.03.244.
2. Mourtzis D., Doukas M., Psarommatis F., Manufacturing Network Design for Mass Customisation using a Genetic Algorithm and an Intelligent Search Method, Procedia CIRP, vol. 7, 2013, pp. 37–42. Available at: https://doi.org/10.1016/j.procir.2013.05.007.
3. Weipeng Cao, Xizhao Wang, Zhong Ming, Jinzhu Gao,A review on neural networks with random weights, Neurocomputing, Volume 275, 2018, pp. 278–287, ISSN 0925-2312. Available at: https://doi.org/ 10.1016/j.neucom.2017.08.040.
4. Tomasz Dudek, Tygran Dzhuguryan, Justyna Lemke, Sustainable production network design for city multi-floor manufacturing cluster, Procedia Computer Science, vol. 159, 2019, pp. 2081–2090. Available at: https://doi.org/10.1016/j.procs.2019.09.381.
5. Xiaona Song, Peng Sun, Shuai Song, Vladimir Stojanovic,Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance, Journal of the Franklin Institute, vol. 359, issue 9, 2022, , pp. 4138–4159, ISSN 0016-0032. Available at: https://doi.org/10.1016/j.jfranklin.2022.04.003.
6. Fei Han, Jing Jiang, Qing-Hua Ling, Ben-Yue Su, A survey on metaheuristic optimization for random single-hidden layer feedforward neural network, Neurocomputing, vol. 335, 2019, pp, 261–273, ISSN 0925-2312. Available at: https://doi.org/10.1016/j.neucom.2018.07.080.
7. Cristiana L. Lara, David E. Bernal, Can Li, Ignacio E. Grossmann, Global optimization algorithm for multi-period design and planning of centralized and distributed manufacturing networks, Computers & Chemical Engineering, Volume 127, 2019, Pages 295-310, https://doi.org/10.1016/j.compchemeng.2019.05.022.
8. Jiya Yu, Jiye Zhang, Aijing Shu, Yujie Chen, Jianneng Chen, Yongjie Yang, Wei Tang, Yanchao Zhang,Study of convolutional neural network-based semantic segmentation methods on edge intelligence devices for field agricultural robot navigation line extraction, Computers and Electronics in Agriculture, vol. 209, 2023, 107811, ISSN 0168-1699. Available at: https://doi.org/10.1016/j.compag.2023.107811.
9. Ahmed Temtam, Abdraouf Abusoua, Khaled Benyounis, Abdalmonem Tamtam,Use of neural networks and artificial intelligence tools for modeling, characterization, and predicting in material engineering, Reference Module in Materials Science and Materials Engineering, Elsevier, 2023, ISBN 9780128035818. Available at: https://doi.org/10.1016/B978-0-323-96020-5.00088-1.
10. Yohanes Kristianto, Angappa Gunasekaran, Petri Helo, Maqsood Sandhu, A decision support system for integrating manufacturing and product design into the reconfiguration of the supply chain networks, Decision Support Systems, vol. 52, iss. 4, 2012, pp. 790–801. Available at: https://doi.org/10.1016/ j.dss.2011.11.014.
11. Alexandra Birkmaier, Bernhard Oberegger, Andreas Felsberger, Gerald Reiner, Wilfried Sihn, Towards a robust digital production and logistics network by implementing flexibility measures, Procedia CIRP, vol. 104, 2021, pp. 1310–1315. Available at: https://doi.org/10.1016/j.procir.2021.11.220.
12. Martsenyuk V. and Klos-Witkowska A. “Computation Model of Cyber-Physical Immunosensor System”, in IEEE Access, vol. 7, pp. 62325–62337, 2019. Doi: 10.1109/ACCESS.2019.2915946.
13. Koch Y., Wolf T., Sorger P., Eils R., Brors B. Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states (2013) PLoS ONE, 8 (12), art. no. e82593. Doi: 10.1371/journal.pone.0082593.
14. Martsenyuk V., Warwas K., Augustynek K., Klos-Witkowska A., Karpinskyi V., Klymuk N., Mayhruk Z. On multivariate method of qualitative analysis of Hodgkin-Huxley model with decision tree induction (2016) International Conference on Control, Automation and Systems, 0, art. no. 7832365, pp. 489–494. Doi: 10.1109/ICCAS.2016.7832365.
15. Lyapandra A. S., Martsenyuk V. P., Gvozdetska I. S., Szklarczyk R., Rajba S. A. Qualitative analysis of compartmental dynamic system using decision-tree induction (2015) Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2015, 2, art. no. 7341391, pp. 688–692. Doi: 10.1109/IDAACS.2015.7341391.
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| References: |
1. Jelena Milisavljevic-Syed, Janet K. Allen, Sesh Commuri, Farrokh Mistree, Design of networked manufacturing systems for Industry 4.0, Procedia CIRP, vol. 81, 2019, рр. 1016–1021. Available at: https://doi.org/10.1016/j.procir.2019.03.244.
2. Mourtzis D., Doukas M., Psarommatis F., Manufacturing Network Design for Mass Customisation using a Genetic Algorithm and an Intelligent Search Method, Procedia CIRP, vol. 7, 2013, pp. 37–42. Available at: https://doi.org/10.1016/j.procir.2013.05.007.
3. Weipeng Cao, Xizhao Wang, Zhong Ming, Jinzhu Gao,A review on neural networks with random weights, Neurocomputing, Volume 275, 2018, pp. 278–287, ISSN 0925-2312. Available at: https://doi.org/ 10.1016/j.neucom.2017.08.040.
4. Tomasz Dudek, Tygran Dzhuguryan, Justyna Lemke, Sustainable production network design for city multi-floor manufacturing cluster, Procedia Computer Science, vol. 159, 2019, pp. 2081–2090. Available at: https://doi.org/10.1016/j.procs.2019.09.381.
5. Xiaona Song, Peng Sun, Shuai Song, Vladimir Stojanovic,Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance, Journal of the Franklin Institute, vol. 359, issue 9, 2022, , pp. 4138–4159, ISSN 0016-0032. Available at: https://doi.org/10.1016/j.jfranklin.2022.04.003.
6. Fei Han, Jing Jiang, Qing-Hua Ling, Ben-Yue Su, A survey on metaheuristic optimization for random single-hidden layer feedforward neural network, Neurocomputing, vol. 335, 2019, pp, 261–273, ISSN 0925-2312. Available at: https://doi.org/10.1016/j.neucom.2018.07.080.
7. Cristiana L. Lara, David E. Bernal, Can Li, Ignacio E. Grossmann, Global optimization algorithm for multi-period design and planning of centralized and distributed manufacturing networks, Computers & Chemical Engineering, Volume 127, 2019, Pages 295-310, https://doi.org/10.1016/j.compchemeng.2019.05.022.
8. Jiya Yu, Jiye Zhang, Aijing Shu, Yujie Chen, Jianneng Chen, Yongjie Yang, Wei Tang, Yanchao Zhang,Study of convolutional neural network-based semantic segmentation methods on edge intelligence devices for field agricultural robot navigation line extraction, Computers and Electronics in Agriculture, vol. 209, 2023, 107811, ISSN 0168-1699. Available at: https://doi.org/10.1016/j.compag.2023.107811.
9. Ahmed Temtam, Abdraouf Abusoua, Khaled Benyounis, Abdalmonem Tamtam,Use of neural networks and artificial intelligence tools for modeling, characterization, and predicting in material engineering, Reference Module in Materials Science and Materials Engineering, Elsevier, 2023, ISBN 9780128035818. Available at: https://doi.org/10.1016/B978-0-323-96020-5.00088-1.
10. Yohanes Kristianto, Angappa Gunasekaran, Petri Helo, Maqsood Sandhu, A decision support system for integrating manufacturing and product design into the reconfiguration of the supply chain networks, Decision Support Systems, vol. 52, iss. 4, 2012, pp. 790–801. Available at: https://doi.org/10.1016/ j.dss.2011.11.014.
11. Alexandra Birkmaier, Bernhard Oberegger, Andreas Felsberger, Gerald Reiner, Wilfried Sihn, Towards a robust digital production and logistics network by implementing flexibility measures, Procedia CIRP, vol. 104, 2021, pp. 1310–1315. Available at: https://doi.org/10.1016/j.procir.2021.11.220.
12. Martsenyuk V. and Klos-Witkowska A. “Computation Model of Cyber-Physical Immunosensor System”, in IEEE Access, vol. 7, pp. 62325–62337, 2019. Doi: 10.1109/ACCESS.2019.2915946.
13. Koch Y., Wolf T., Sorger P., Eils R., Brors B. Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states (2013) PLoS ONE, 8 (12), art. no. e82593. Doi: 10.1371/journal.pone.0082593.
14. Martsenyuk V., Warwas K., Augustynek K., Klos-Witkowska A., Karpinskyi V., Klymuk N., Mayhruk Z. On multivariate method of qualitative analysis of Hodgkin-Huxley model with decision tree induction (2016) International Conference on Control, Automation and Systems, 0, art. no. 7832365, pp. 489–494. Doi: 10.1109/ICCAS.2016.7832365.
15. Lyapandra A. S., Martsenyuk V. P., Gvozdetska I. S., Szklarczyk R., Rajba S. A. Qualitative analysis of compartmental dynamic system using decision-tree induction (2015) Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2015, 2, art. no. 7341391, pp. 688–692. Doi: 10.1109/IDAACS.2015.7341391.
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