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Dynamic neural network-based system identification of a pressurized water reactor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

This work presents a dynamic neural network-based (DNN) system identification approach for a pressurized water nuclear reactor. The presented empirical modelling approach describes the DNN structure using differential equations. Local optimization algorithms based on unconstrained Quasi-Newton and interior point approaches are used in the identification process. The efficacy of the proposed approach has been demonstrated by identifying a nuclear reactor core coupled with thermal-hydraulics. DNNs are employed to train the structure and validate it using the nuclear reactor data. The simulation results show that the neural network identified model is sufficiently able to capture the dynamics of the nuclear reactor and it is suitably able to approximate the complex nuclear reactor system.
Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Control, Mechatronics and Automation, (ICCMA 2020)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)978-1-7281-9210-9, 978-1-7281-9209-3
ISBN (Print)978-1-7281-9211-6
Publication statusPublished - 29 Dec 2020
EventThe 8th International Conference on Control, Mechatronics and Automation - Moscow, Russian Federation
Duration: 6 Nov 20208 Nov 2020


ConferenceThe 8th International Conference on Control, Mechatronics and Automation
Abbreviated titleICCMA 2020
CountryRussian Federation
Internet address



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    Accepted author manuscript (Post-print), 798 KB, PDF document

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