Model guided deep learning approach towards prediction of physical system behavior

Subhasish Das; Anurag Agrawal; Ayan Banerjee; Sandeep Gupta

doi:10.1109/ICMLA.2017.000-5

Model guided deep learning approach towards prediction of physical system behavior

Subhasish Das, Anurag Agrawal, Ayan Banerjee, Sandeep Gupta

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Cyber-physical control systems involve a discrete computational algorithm to control continuous physical systems. Often the control algorithm uses predictive models of the physical system in its decision making process. However, physical system models suffer from several inaccuracies when employed in practice. Mitigating such inaccuracies is often difficult and have to be repeated for different instances of the physical system. In this paper, we propose a model guided deep learning method for extraction of accurate prediction models of physical systems, in presence of artifacts observed in real life deployments. Given an initial potentially suboptimal mathematical prediction model, our model guided deep learning method iteratively improves the model through a data driven training approach. We apply the proposed approach on the closed loop blood glucose control system. Using this proposed approach, we achieve an improvement over predictive Bergman Minimal Model by a factor of around 100.

Original language	English (US)
Title of host publication	Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Editors	Xuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1079-1082
Number of pages	4
ISBN (Electronic)	9781538614174
DOIs	https://doi.org/10.1109/ICMLA.2017.000-5
State	Published - 2017
Event	16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico Duration: Dec 18 2017 → Dec 21 2017

Publication series

Name	Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Volume	2017-December

Other

Other	16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Country/Territory	Mexico
City	Cancun
Period	12/18/17 → 12/21/17

Keywords

Artificial neural networks
deep learning
physical systems
prediction

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ICMLA.2017.000-5

Cite this

Das, S., Agrawal, A., Banerjee, A., & Gupta, S. (2017). Model guided deep learning approach towards prediction of physical system behavior. In X. Chen, B. Luo, F. Luo, V. Palade, & M. A. Wani (Eds.), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 (pp. 1079-1082). (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2017.000-5

Model guided deep learning approach towards prediction of physical system behavior. / Das, Subhasish; Agrawal, Anurag; Banerjee, Ayan et al.
Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. ed. / Xuewen Chen; Bo Luo; Feng Luo; Vasile Palade; M. Arif Wani. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1079-1082 (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Das, S, Agrawal, A, Banerjee, A & Gupta, S 2017, Model guided deep learning approach towards prediction of physical system behavior. in X Chen, B Luo, F Luo, V Palade & MA Wani (eds), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 1079-1082, 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, Cancun, Mexico, 12/18/17. https://doi.org/10.1109/ICMLA.2017.000-5

Das S, Agrawal A, Banerjee A , Gupta S. Model guided deep learning approach towards prediction of physical system behavior. In Chen X, Luo B, Luo F, Palade V, Wani MA, editors, Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1079-1082. (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017). doi: 10.1109/ICMLA.2017.000-5

Das, Subhasish ; Agrawal, Anurag ; Banerjee, Ayan et al. / Model guided deep learning approach towards prediction of physical system behavior. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. editor / Xuewen Chen ; Bo Luo ; Feng Luo ; Vasile Palade ; M. Arif Wani. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1079-1082 (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017).

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N2 - Cyber-physical control systems involve a discrete computational algorithm to control continuous physical systems. Often the control algorithm uses predictive models of the physical system in its decision making process. However, physical system models suffer from several inaccuracies when employed in practice. Mitigating such inaccuracies is often difficult and have to be repeated for different instances of the physical system. In this paper, we propose a model guided deep learning method for extraction of accurate prediction models of physical systems, in presence of artifacts observed in real life deployments. Given an initial potentially suboptimal mathematical prediction model, our model guided deep learning method iteratively improves the model through a data driven training approach. We apply the proposed approach on the closed loop blood glucose control system. Using this proposed approach, we achieve an improvement over predictive Bergman Minimal Model by a factor of around 100.

AB - Cyber-physical control systems involve a discrete computational algorithm to control continuous physical systems. Often the control algorithm uses predictive models of the physical system in its decision making process. However, physical system models suffer from several inaccuracies when employed in practice. Mitigating such inaccuracies is often difficult and have to be repeated for different instances of the physical system. In this paper, we propose a model guided deep learning method for extraction of accurate prediction models of physical systems, in presence of artifacts observed in real life deployments. Given an initial potentially suboptimal mathematical prediction model, our model guided deep learning method iteratively improves the model through a data driven training approach. We apply the proposed approach on the closed loop blood glucose control system. Using this proposed approach, we achieve an improvement over predictive Bergman Minimal Model by a factor of around 100.

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Model guided deep learning approach towards prediction of physical system behavior

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