RBF Neural Network Algorithm for Handwritten Digit Recognition

Application of RBF Neural Networks in Handwritten Digit Recognition

The RBF (Radial Basis Function) neural network is a local approximation-based neural network model commonly used for classification and function approximation tasks. In handwritten digit recognition, RBF neural networks are widely adopted due to their fast training speed and excellent generalization capabilities.

Core Algorithm Implementation

Data Preprocessing: Handwritten digit images typically require grayscale conversion, binarization, and normalization processing to meet neural network input requirements. In code implementation, this involves using image processing functions like rgb2gray() and imbinarize(), followed by scaling pixel values to a standardized range (e.g., [0,1]).

RBF Network Architecture: The network consists of an input layer, hidden layer, and output layer. The hidden layer employs radial basis functions (such as Gaussian functions) as activation functions, which efficiently map input features to high-dimensional spaces for better classification. The MATLAB implementation typically uses newrbf() or similar functions to construct the network structure.

Training Process: The algorithm uses unsupervised learning methods like K-means to determine hidden layer center points, followed by weight adjustment for the output layer through pseudo-inverse or gradient descent methods. Code implementation involves using kmeans() for center initialization and train() or adapt() functions for weight optimization.

Classification Decision: The output layer typically employs Softmax function to convert neural network outputs into probability distributions, ultimately selecting the class with the highest probability as the recognition result. This can be implemented using softmax() function followed by max() function for final classification.

MATLAB Implementation Advantages MATLAB provides comprehensive matrix operations and neural network toolbox, facilitating rapid prototyping of RBF networks. Its built-in optimization algorithms significantly enhance training efficiency, making it particularly suitable for academic research and small-scale experiments. Key functions like rbf() and patternnet() simplify network creation and training processes.