Function Approximation using General Regression Neural Networks (GRNN)

This project aimed to employ General Regression Neural Networks (GRNN) to predict output values based on discrete input characteristics by implementing GRNN models to approximate linear, quadratic, cubic, polynomial, and trigonometric functions using Python and developing regression programs for the function types, training the models with GRNN, and evaluating their performance.