增强回归元加权最小回归网络在自动焊接参数估计中的应用
Minimalist Regression Network with Reinforced Gradients and Weighted Estimates: a Case Study on Parameters Estimation in Automated Welding
摘要：
本文提出了一种最小神经回归网络，它是同时训练的独立相同回归块的集合。此外，它引入了一个新的乘法参数，由给定层的所有神经单元共享，以保持其梯度的质量。此外，它通过学习权重因子来提高其估计精度，该权重因子的数量在每次训练迭代时捕获估计值和实际值之间的冗余。我们选择不同的焊接技术的直接焊接参数的估计，以显示显着改善这些参数的计算，我们的模型相比，在文献中的国家的最先进的技术。此外，我们证明了我们的模型的能力，以保持其性能时，提出了不同的焊接技术的组合数据。这是一个不平凡的结果，在获得一个可扩展的模型，其质量的估计是独立的采用焊接技术。
英文摘要：
This paper presents a minimalist neural regression network as an aggregate of independent identical regression blocks that are trained simultaneously. Moreover, it introduces a new multiplicative parameter, shared by all the neural units of a given layer, to maintain the quality of its gradients. Furthermore, it increases its estimation accuracy via learning a weight factor whose quantity captures the redundancy between the estimated and actual values at each training iteration. We choose the estimation of the direct weld parameters of different welding techniques to show a significant improvement in calculation of these parameters by our model in contrast to state-of-the-arts techniques in the literature. Furthermore, we demonstrate the ability of our model to retain its performance when presented with combined data of different welding techniques. This is a nontrivial result in attaining an scalable model whose quality of estimation is independent of adopted welding techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
