论文标题
用于剥离计算的贝叶斯正则化神经网络模型
A Bayesian regularization-backpropagation neural network model for peeling computations
论文作者
论文摘要
使用贝叶斯正则化 - 折叠神经网络（BR-BPNN）模型来预测壁虎刮刀剥离的某些方面。最大正态和切向切除力的变化以及与剥离角脱离时的产量角度的变化。 K折交叉验证用于提高模型的有效性。输入数据取自有限元（Fe）剥离结果。神经网络接受了75％的FE数据集培训。其余25％用于预测剥离行为。对隐藏层神经元数量的每一更改评估训练性能，以确定最佳网络结构。计算相对误差以对预测结果和FE结果进行清晰的比较。结果表明，与K折技术结合使用的BR-BPNN模型具有估计剥离行为的重要潜力。
Bayesian regularization-backpropagation neural network (BR-BPNN) 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 k-fold technique has significant potential to estimate the peeling behavior.
