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Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2021-07-19 , DOI: arxiv-2107.09484
Gabriel Kronberger, Michael Kommenda, Andreas Promberger, Falk Nickel

Friction systems are mechanical systems wherein friction is used for force transmission (e.g. mechanical braking systems or automatic gearboxes). For finding optimal and safe design parameters, engineers have to predict friction system performance. This is especially difficult in real-world applications, because it is affected by many parameters. We have used symbolic regression and genetic programming for finding accurate and trustworthy prediction models for this task. However, it is not straight-forward how nominal variables can be included. In particular, a one-hot-encoding is unsatisfactory because genetic programming tends to remove such indicator variables. We have therefore used so-called factor variables for representing nominal variables in symbolic regression models. Our results show that GP is able to produce symbolic regression models for predicting friction performance with predictive accuracy that is comparable to artificial neural networks. The symbolic regression models with factor variables are less complex than models using a one-hot encoding.

中文翻译:

使用符号回归和因子变量遗传规划预测摩擦系统性能

摩擦系统是机械系统,其中摩擦用于力传递(例如机械制动系统或自动变速箱)。为了找到最佳和安全的设计参数,工程师必须预测摩擦系统性能。这在实际应用中尤其困难,因为它受许多参数的影响。我们使用符号回归和遗传编程来为这项任务找到准确可靠的预测模型。然而,如何包括名义变量并不是直接的。特别是,one-hot-encoding 是不能令人满意的,因为遗传编程倾向于删除这些指示变量。因此,我们使用所谓的因子变量来表示符号回归模型中的名义变量。我们的结果表明,GP 能够生成符号回归模型来预测摩擦性能,其预测精度可与人工神经网络相媲美。具有因子变量的符号回归模型没有使用 one-hot 编码的模型复杂。
更新日期:2021-07-21
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