Radial Function

Radial functions are mathematical functions whose value depends only on the distance from a central point, finding applications across diverse fields. Current research focuses on improving their use in machine learning, particularly within deep neural networks and Kolmogorov-Arnold networks, where they enhance model accuracy and stability for tasks like image classification and traffic prediction. This involves exploring different architectures, such as deep radial basis function networks and hybrid models combining radial basis functions with other functions like B-splines, to optimize performance and address limitations like convergence issues and high condition numbers in certain applications. The improved efficiency and accuracy offered by these advancements have significant implications for various scientific domains and practical applications requiring complex data analysis and prediction.