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http://dx.doi.org/10.6109/jkiice.2020.24.6.809

A NARX Dynamic Neural Network Platform for Small-Sat PDM  

Lee, Hae-Jun (Department of SaTReC, Korea Advanced Institute Science and Technology)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.6, 2020 , pp. 809-817 More about this Journal
Abstract
In the design and development process of Small-Sat power distribution and transmission module, the stability of dynamic resources was evaluated by a deep learning algorithm. The requirements for the stability evaluation consisted of the power distribution function of the power distribution module and demand module to the SAR radar in Small-Sat. To verify the performance of the switching power components constituting the power module PDM, the reliability was verified using a dynamic neural network. The adoption material of deep learning for reliability verification is the power distribution function of the payload to the power supplied from the small satellite main body. Modeling targets for verifying the performance of this function are output voltage (slew rate control), voltage error, and load power characteristics. First, to this end, the Coefficient Structure area was defined by modeling, and PCB modules were fabricated to compare stability and reliability. Second, Levenberg-Marquare based Two-Way NARX neural network Sigmoid Transfer was used as a deep learning algorithm.
Keywords
SAR; Dynamic Neural Network; Small-Sat Power; NARX Network; Power Distribution Module Convergence;
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