SechKAN: Kolmogorov-Arnold Networks with Hyperbolic Secant Functions
In recent years KolmogorovArnold Networks KANs have attracted increasing attention due to their effectiveness in machine learning and scientific computing offering a new paradigm for neural network design In this paper we present SechKAN a novel KAN based on hyperbolic secant sech functions The hyperbolic secant basis is adopted for its smooth bellshaped form localized responses and wellbehaved gradients We employ a 1D linear projection to reduce the number of parameters allowing SechKAN to maintain a model size comparable to that of multilayer perceptrons MLPs Experimental results show the effectiveness of SechKAN on function fitting PDE surrogate modeling and image classification benchm...
| Authors | Hoang-Thang Ta |
|---|---|
| Primary category | cs.LG |
| Published | 30 June 2026 |
| Last updated at source | 24 September 2026 |
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