We present a novel approach to predicting the pressure and flow rate of flexible electrohydrodynamic pumps using the Kolmogorov-Arnold Network. Inspired by the Kolmogorov-Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectively than traditional models like Multi-Layer Perceptron and Random Forest. We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.001 for pressure and flow rate predictions, respectively. The symbolic formulas extracted from KAN provided insights into the nonlinear relationships between input parameters and pump performance. These findings demonstrate that KAN offers exceptional accuracy and interpretability, making it a promising alternative for predictive modeling in electrohydrodynamic pumping.

翻译：我们提出了一种利用Kolmogorov-Arnold网络预测柔性电液动力泵压力和流量的新方法。受Kolmogorov-Arnold表示定理启发，KAN以基于可学习样条的激活函数替代固定激活函数，使其能比多层感知机和随机森林等传统模型更有效地逼近复杂非线性函数。我们在柔性电液动力泵参数数据集上评估了KAN，并将其性能与随机森林和多层感知机模型进行对比。KAN实现了卓越的预测精度，其压力预测与流量预测的均方误差分别为12.186和0.001。从KAN中提取的符号公式揭示了输入参数与泵性能之间的非线性关系。这些结果表明，KAN具有优异的精度与可解释性，为电液动力泵领域的预测建模提供了具有前景的替代方案。