In this paper, we introduce BSRBF-KAN, a Kolmogorov Arnold Network (KAN) that combines B-splines and radial basis functions (RBFs) to fit input vectors during data training. We perform experiments with BSRBF-KAN, multi-layer perception (MLP), and other popular KANs, including EfficientKAN, FastKAN, FasterKAN, and GottliebKAN over the MNIST and Fashion-MNIST datasets. BSRBF-KAN shows stability in 5 training runs with a competitive average accuracy of 97.55% on MNIST and 89.33% on Fashion-MNIST and obtains convergence better than other networks. We expect BSRBF-KAN to open many combinations of mathematical functions to design KANs. Our repo is publicly available at: https://github.com/hoangthangta/BSRBF_KAN.

翻译：本文提出BSRBF-KAN，一种结合B样条与径向基函数的Kolmogorov-Arnold网络，用于在数据训练过程中拟合输入向量。我们在MNIST和Fashion-MNIST数据集上对BSRBF-KAN、多层感知机及其他主流KAN模型（包括EfficientKAN、FastKAN、FasterKAN和GottliebKAN）进行了实验。BSRBF-KAN在5次训练运行中表现出稳定性，在MNIST上取得了97.55%的平均准确率，在Fashion-MNIST上取得了89.33%的平均准确率，其收敛性优于其他网络。我们期望BSRBF-KAN能为设计KAN模型开启多种数学函数组合的可能性。代码仓库已公开于：https://github.com/hoangthangta/BSRBF_KAN。