Structural Engineering and Mechanics

  • 제83권2호
  • /
  • Pages.259-272
  • /
  • 2022
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Deep neural networks trained by the adaptive momentum-based technique for stability simulation of organic solar cells

  • Xu, Peng (School of Electrical Engineerin, Jilin Engineering Normal University) ;
  • Qin, Xiao (School of Electrical Engineerin, Jilin Engineering Normal University) ;
  • Zhu, Honglei (Planning Paint Shop Planning Department, FAW-Volkswagen Automotive Co. LTD)
  • 투고 : 2021.07.17
  • 심사 : 2022.05.24
  • 발행 : 2022.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

The branch of electronics that uses an organic solar cell or conductive organic polymers in order to yield electricity from sunlight is called photovoltaic. Regarding this crucial issue, an artificial intelligence-based predictor is presented to investigate the vibrational behavior of the organic solar cell. In addition, the generalized differential quadrature method (GDQM) is utilized to extract the results. The validation examination is done to confirm the credibility of the results. Then, the deep neural network with fully connected layers (DNN-FCL) is trained by means of Adam optimization on the dataset whose members are the vibration response of the design-points. By determining the optimum values for the biases along with weights of DNN-FCL, one can predict the vibrational characteristics of any organic solar cell by knowing the properties defined as the inputs of the mentioned DNN. To assess the ability of the proposed artificial intelligence-based model in prediction of the vibrational response of the organic solar cell, the authors monitored the mean squared error in different steps of the training the DNN-FCL and they observed that the convergency of the results is excellent.

키워드

과제정보

This work was supported by Project of Science and Technology Development Plan of Jilin Province (Grant nos. 20180520226JH), Doctoral Project.

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