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

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arxiv 2406.11173 v5 pith:BARIF7BV submitted 2024-06-17 cs.CL

Hoang-Thang Ta This is my paper · ORCID
classification cs.CL
keywords bsrbf-kanfunctionsb-splinesbasisfashion-mnistkansmnistnetworks
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States

    quant-ph 2025-06 conditional novelty 6.0 of 10

    SineKAN, a Kolmogorov-Arnold network with sinusoidal activations, accurately represents ground states of 1D spin chains and outperforms RBM, LSTM, and MLP neural quantum states in the J1-J2 model.

  2. Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks

    quant-ph 2025-09 reject novelty 5.0 of 10

    QKANs show strong empirical performance on regression, vision, and language tasks, but the claimed exponential parameter reduction is not rigorously established.

  3. Toroidal area-preserving parameterizations of genus-one closed surfaces

    math.NA 2025-08 unverdicted novelty 5.0 of 10

    Four Riemannian optimization algorithms (projected/Riemannian gradient and conjugate gradient) are proposed to compute toroidal area-preserving parameterizations by minimizing stretch energy on a power manifold of ring tori.

  4. Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment

    eess.IV 2025-05 conditional novelty 4.0 of 10

    A Taylor-expansion KAN variant outperforms B-spline, orthogonal-polynomial, wavelet, and Fourier KAN variants and MLPs on five authentic BIQA databases, with PCA and shallow layers reducing training cost.

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