Predictive Modeling of Flexible EHD Pumps using Kolmogorov-Arnold Networks
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
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| _version_ | 1866912170989584384 |
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| author | Peng, Yanhong Wang, Yuxin Hu, Fangchao He, Miao Mao, Zebing Huang, Xia Ding, Jun |
| author_facet | Peng, Yanhong Wang, Yuxin Hu, Fangchao He, Miao Mao, Zebing Huang, Xia Ding, Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07488 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predictive Modeling of Flexible EHD Pumps using Kolmogorov-Arnold Networks Peng, Yanhong Wang, Yuxin Hu, Fangchao He, Miao Mao, Zebing Huang, Xia Ding, Jun Machine Learning Robotics Symbolic Computation 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. |
| title | Predictive Modeling of Flexible EHD Pumps using Kolmogorov-Arnold Networks |
| topic | Machine Learning Robotics Symbolic Computation |
| url | https://arxiv.org/abs/2405.07488 |