Accurate approximation of complex nonlinear functions is a fundamental challenge across many scientific and engineering domains. Traditional neural network architectures often struggle to capture intricate patterns and irregularities present in high-dimensional functions. This paper introduces the Chebyshev Kolmogorov-Arnold Network (Chebyshev KAN), a novel approach that combines the theoretical foundations of the Kolmogorov-Arnold Theorem with the powerful approximation capabilities of Chebyshev polynomials. 1

翻译：复杂非线性函数的高精度逼近是众多科学与工程领域的基础挑战。传统神经网络架构往往难以捕捉高维函数中存在的复杂模式与不规则特性。本文提出切比雪夫Kolmogorov-Arnold网络（Chebyshev KAN），这是一种融合Kolmogorov-Arnold定理理论基础与切比雪夫多项式强大逼近能力的新型方法。