Handwritten Digit Recognition Using RBF Network in MATLAB

This MATLAB program implements handwritten digit recognition using a Radial Basis Function (RBF) neural network approach. The system processes input handwritten digit images by first converting them into numerical matrix representations through image preprocessing techniques, which may include normalization and feature extraction. The core implementation employs RBF network training using MATLAB's neural network toolbox functions, where the network learns characteristic patterns through supervised learning with labeled training data. The trained model establishes decision boundaries in feature space using Gaussian kernel functions to distinguish between digit classes. During recognition phase, the program compares input digit features against the trained model using distance metrics and activation functions to accurately classify unknown digits. The implementation demonstrates key MATLAB functions including image processing routines for data preparation, newrb or similar functions for RBF network creation, and simulation functions for classification. This program provides practical insights into applying RBF networks for pattern recognition tasks while showcasing MATLAB's capabilities for implementing machine learning solutions in digital image recognition applications.