Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks
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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Biomimetic Intelligence and Robotics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667379724000421 |
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| _version_ | 1850101069917454336 |
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| author | Yanhong Peng Yuxin Wang Fangchao Hu Miao He Zebing Mao Xia Huang Jun Ding |
| author_facet | Yanhong Peng Yuxin Wang Fangchao Hu Miao He Zebing Mao Xia Huang Jun Ding |
| author_sort | Yanhong Peng |
| collection | DOAJ |
| description | 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 0.012 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 | Article |
| id | doaj-art-acf3cd2ba81040e19e378a942a266d14 |
| institution | DOAJ |
| issn | 2667-3797 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomimetic Intelligence and Robotics |
| spelling | doaj-art-acf3cd2ba81040e19e378a942a266d142025-08-20T02:40:08ZengElsevierBiomimetic Intelligence and Robotics2667-37972024-12-014410018410.1016/j.birob.2024.100184Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold NetworksYanhong Peng0Yuxin Wang1Fangchao Hu2Miao He3Zebing Mao4Xia Huang5Jun Ding6College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China; Corresponding author.Department of Mechanical Systems Engineering, Nagoya University, Tokai National Higher Education and Research, Nagoya 464-8603, Japan; School of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaFaculty of Engineering, Yamaguchi University, Yamaguchi 755-8611, JapanCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaWe 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 0.012 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.http://www.sciencedirect.com/science/article/pii/S2667379724000421Kolmogorov–Arnold NetworksElectrohydrodynamic pumpsNeural network |
| spellingShingle | Yanhong Peng Yuxin Wang Fangchao Hu Miao He Zebing Mao Xia Huang Jun Ding Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks Biomimetic Intelligence and Robotics Kolmogorov–Arnold Networks Electrohydrodynamic pumps Neural network |
| title | Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks |
| title_full | Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks |
| title_fullStr | Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks |
| title_full_unstemmed | Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks |
| title_short | Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks |
| title_sort | predictive modeling of flexible ehd pumps using kolmogorov arnold networks |
| topic | Kolmogorov–Arnold Networks Electrohydrodynamic pumps Neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2667379724000421 |
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