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Abstract
A Bayesian regularization-backpropagation neural network (BRBPNN) model is employed to predict some aspects of the gecko spatula peeling, viz. the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. K-fold cross validation is used to improve the effectiveness of the model. The input data is taken from finite element (FE) peeling results. The neural network is trained with 75% of the FE dataset. The remaining 25% are utilized to predict the peeling behavior. The training performance is evaluated for every change in the number of hidden layer neurons to determine the optimal network structure The relative error is calculated to draw a clear comparison between predicted and FE results. It is shown that the BR-BPNN model in conjunction with the k-fold technique has significant potential to estimate the peeling behavior.
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Web of Science
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Authors (4)
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Saipraneeth Gouravaraju
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Jyotindra Narayan
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Sachin Singh Gautam
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/00218464.2021.2001335
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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JOURNAL OF ADHESION
no. 99,
pages 92 - 115,
ISSN: 0021-8464 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Gouravaraju S., Narayan J., Sauer R., Gautam S. S.: A Bayesian regularization-backpropagation neural network model for peeling computations// JOURNAL OF ADHESION -Vol. 99,iss. 1 (2023), s.92-115
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/00218464.2021.2001335
- Sources of funding:
-
- COST_FREE
- Verified by:
- Gdańsk University of Technology
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