Radial Basis Function Networks

Radial Basis Function Networks (RBFNs) are a type of artificial neural network used primarily for tasks like function approximation and classification. They work by taking data inputs and transforming them based on distances from certain points, called "centers." Each center responds differently to inputs, allowing the network to learn complex patterns. The output is determined by combining these responses, making RBFNs effective for recognizing trends in data. They are useful in various fields, including finance, engineering, and image processing, due to their ability to model non-linear relationships effectively.