RBF Networks for Function Approximation

This is an RBF network program designed for function approximation! The implementation includes several algorithms related to RBF networks that will help you better understand and apply this network architecture. RBF networks are commonly used neural network models that can learn complex functional relationships from sample data through radial basis function transformations. The program provides multiple algorithm options, allowing you to select the appropriate approach for your specific function approximation needs. Key implemented features include center selection methods, weight calculation algorithms, and spread parameter optimization techniques. Whether you're using RBF networks in research or engineering practice, this program offers convenient and efficient tools with clear code structure and documented implementation approaches. Through using this program, you'll gain deeper insights into RBF network principles and their practical applications, with well-commented code demonstrating critical aspects like Gaussian activation functions and linear output combinations.