Bayesian regularization-backpropagation neural network (BR-BPNN), a machine learning algorithm, is employed to predict some aspects of the gecko spatula peeling such as the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. 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 observed that BR-BPNN models have significant potential to estimate the peeling behavior.

中文翻译：

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用于剥离计算的贝叶斯正则化-反向传播神经网络模型

贝叶斯正则化-反向传播神经网络 (BR-BPNN) 是一种机器学习算法，用于预测壁虎刮刀剥离的某些方面，例如最大法向和切向拉脱力的变化以及分离时的合力角与剥离角度。输入数据取自有限元 (FE) 剥离结果。神经网络使用 75% 的 FE 数据集进行训练。剩余的 25% 用于预测剥离行为。对于隐藏层神经元数量的每次变化，都会评估训练性能，以确定最佳网络结构。计算相对误差以在预测结果和有限元结果之间进行明确比较。据观察，BR-BPNN 模型具有估计剥离行为的巨大潜力。