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.
翻译:使用BR-BPNN)模型来预测Gecko spatula剥皮神经网络(BR-BPNN)的某些方面,即最大正常和近距离拉动力的变异和与剥皮角分离后产生的力角。 K- 交叉校验用于提高模型的有效性。输入数据取自一定元素(FE)剥皮结果。神经网络培训使用75%的FE数据集。其余25%用于预测剥皮行为。对培训绩效进行评估,以确定隐藏层神经的每一个变化,以确定最佳网络结构。计算相对错误是为了对预测结果和FE结果进行清晰的比较。显示BR- BPNN模型与K- 倍技术一道,有很大潜力来估计剥皮行为。