A Bayesian regularization-backpropagation neural network model for peeling computations
نویسندگان
چکیده
A Bayesian regularization-backpropagation neural network (BR-BPNN) model is employed to predict some aspects of the gecko spatula peeling, viz. variation maximum normal and tangential pull-off forces resultant force angle at detachment with peeling angle. K-fold cross validation used improve effectiveness model. The input data taken from finite element (FE) results. trained 75% FE dataset. remaining 25% are utilized behavior. training performance evaluated for every change in number hidden layer neurons determine optimal structure. relative error calculated draw a clear comparison between predicted It shown that BR-BPNN conjunction k-fold technique has significant potential estimate
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ژورنال
عنوان ژورنال: Journal of Adhesion
سال: 2021
ISSN: ['0021-8464', '1563-518X', '1026-5414']
DOI: https://doi.org/10.1080/00218464.2021.2001335