BSRBF-KAN: A combination of B-splines and Radial Basic Functions in Kolmogorov-Arnold Networks

In this paper, we introduce BSRBF-KAN, a Kolmogorov Arnold Network (KAN) that combines Bsplines and radial basis functions (RBFs) to fit input vectors in data training. We perform experiments with BSRBF-KAN, MLP, and other popular KANs, including EfficientKAN, FastKAN, FasterKAN, and GottliebKAN over the MNIST dataset. BSRBF-KAN shows stability in 5 training times with a competitive average accuracy of 97.55% and obtains convergence better than other networks. We expect BSRBF-KAN can open many combinations of mathematical functions to design KANs. Our repo is publicly available at: https://github.com/hoangthangta/BSRBF-KAN.