Exploring the power of KANs: Overcoming MLP limitations in complex data analysis

Abstract

In the field of artificial intelligence, the requirements of models that can adeptly explore informative features of complex data sets has led to the emergence of advanced neural network architectures. Beyond the perceptron-based architectures that are currently in widespread use, a variety of innovative and cutting-edge designs have been proposed. This paper thoroughly explores the critical role of Kolmogorov-Arnold Networks (KAN) in overcoming the limitations of traditional Multilayer Perceptron (MLP) models, particularly highlighting KANs significant advantages in handling complex nonlinear and high-dimensional data. By analyzing the mathematical foundations and neural network architecture of KAN, and comparing it with MLP, the paper demonstrates KANs outstanding performance in time series analysis and image classification. The research indicates that KAN has distinct advantages in addressing high-dimensional nonlinear data. The paper also summarizes the current research progress on KAN and discusses its enormous potential in future machine learning and real-world applications, pointing out possible future research directions.