RBF Classification with Neural Network Implementation

The article discusses RBF neural networks, which are feedforward networks based on radial basis functions, known for their excellent approximation capabilities and learning efficiency. The fundamental principle involves selecting appropriate radial basis functions to construct the model, followed by parameter optimization using training data to achieve accurate predictions for input data. In code implementations, RBF networks typically utilize Gaussian functions as activation functions in hidden layers, where center selection can employ k-means clustering or random sampling methods. Weight optimization between hidden and output layers commonly uses linear regression or gradient descent algorithms. RBF neural networks find extensive applications in pattern recognition, function approximation, and time series prediction, making them a highly significant and practical neural network model. Key implementation considerations include determining the number of hidden nodes, optimizing spread parameters, and selecting appropriate training algorithms like orthogonal least squares for efficient center selection.