使用Kolmogorov-Arnold网络的柔性EHD泵预测建模
Predictive Modeling of Flexible EHD Pumps using Kolmogorov-Arnold Networks
AI总结：
本文提出用Kolmogorov-Arnold网络预测柔性EHD泵的压力和流量，KAN在精度和可解释性上优于MLP和RF，压力与流量预测均方误差分别为12.186和0.001。
摘要：
我们提出了一种使用Kolmogorov-Arnold Network（KAN）预测柔性电液动力（EHD）泵压力和流量的新方法。受Kolmogorov-Arnold表示定理启发，KAN用可学习的基于样条函数的激活函数替代固定激活函数，使其能够比Multi-Layer Perceptron（MLP）和Random Forest（RF）等传统模型更有效地逼近复杂非线性函数。我们在柔性EHD泵参数数据集上评估了KAN，并将其性能与RF和MLP模型进行了比较。KAN取得了更优的预测精度，压力和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。这些发现表明，KAN具有出色的精度和可解释性，使其成为电液动力泵预测建模中一个有前景的替代方案。
英文摘要：
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.
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。
