RBF Neural Network Function Approximation

RBF neural network function approximation is a widely used machine learning algorithm capable of modeling complex nonlinear functional relationships. In this implementation, we utilize 100 sample points for training the network and another 100 data points for performance evaluation. The RBF network operates by employing radial basis functions as activation functions in the hidden layer, typically using Gaussian functions with adjustable centers and widths. The approximation process involves calculating the Euclidean distance between input vectors and prototype centers, then applying the radial basis function transformation. Through RBF network function approximation, we can effectively capture and predict complex data relationships, providing accurate predictions for unknown data instances. This algorithm finds extensive applications in data mining, predictive analytics, and pattern recognition domains, with common implementations involving center selection algorithms like k-means clustering and linear weight optimization in the output layer.