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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.
|Title of host publication||Proceedings of the 8th International Conference on Control, Mechatronics and Automation, (ICCMA 2020)|
|Publisher||Institute of Electrical and Electronics Engineers|
|Number of pages||5|
|ISBN (Electronic)||978-1-7281-9210-9, 978-1-7281-9209-3|
|Publication status||Published - 29 Dec 2020|
|Event||The 8th International Conference on Control, Mechatronics and Automation - Moscow, Russian Federation|
Duration: 6 Nov 2020 → 8 Nov 2020
|Conference||The 8th International Conference on Control, Mechatronics and Automation|
|Abbreviated title||ICCMA 2020|
|Period||6/11/20 → 8/11/20|
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