Classification of Sample Data Using RBF Neural Network Algorithm

This document presents an approach for classifying sample data using the Radial Basis Function (RBF) neural network algorithm, along with corresponding program implementation for training data classification. The algorithm operates by first training a model using sample data, then utilizing this trained model to classify new datasets. RBF neural networks employ radial basis functions as activation functions in the hidden layer, typically implementing a Gaussian function kernel that measures the distance between input vectors and center points. Key implementation aspects include: - Center selection using k-means clustering or random sampling methods - Width parameter calculation for Gaussian functions based on inter-center distances - Output layer weights determination through linear regression or pseudo-inverse matrix operations The RBF neural network algorithm serves as a powerful classification tool with applications spanning image recognition, natural language processing, and pattern identification. Through proper implementation of RBF networks, we can achieve deeper data comprehension and more accurate classification outcomes. The algorithm's architecture typically involves three layers: input layer for feature reception, hidden layer with radial basis activation functions, and output layer providing classification results.