BP neural network topology structure.

<div><p>In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo flour (BF), the poor compaction and fluidity of BF materials entails the need for accurate discrete element model (DEM) and BF par...

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Bibliographic Details
Main Author:	Lintao Chen (4634617) (author)
Other Authors:	Rui Chen (95310) (author), Xiangwei Mou (17403599) (author), Zhaoxiang Liu (9019424) (author), Xu Ma (45624) (author), Xifeng Wu (110961) (author), Xiangwu Deng (6612770) (author)
Published:	2024
Subjects:	Biophysics Molecular Biology Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small particle size required computing power average particle size actual experimental results physical experimental values mold hole compression bf processing operationsand bf processing operations bf materials entails verify two types sidewall collapse method bf particles causes bamboo processing equipment bamboo flour based discrete element parameters dimensional analysis methods simulated ar obtained research results provide bamboo flour bf particles dimensional analysis physical experiments discrete meta conventional methods compression ratio bfs based bf parameters bf ), magnitude analysis injection method five types 5 types simulation results xlink "> trained using relative error related equipment reach tens poor compaction paper proposes output layer neural network medicinal use input layers fifty groups even billions different bfs calibration method calibrated parameters bfs ranges back propagation aforementioned challenges 81 mm 77 mm 3 %. 293 mm 136 mm 1 mm
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BP neural network topology structure.

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