RBF Neural Network Handwritten Digit Recognition Algorithm

In this paper, I present an RBF neural network handwritten digit recognition algorithm implemented using MATLAB. This algorithm is particularly valuable for its capability to recognize handwritten digits through neural network pattern classification. To utilize this algorithm, users need to input handwritten digit images into the system, after which it performs recognition through its trained network architecture. The implementation employs radial basis functions as activation layers and includes efficient training mechanisms, making it both straightforward and computationally effective. This efficiency allows for broad applications in digital recognition domains. By leveraging this algorithm, researchers can better understand and process handwritten digit data, providing significant convenience for both academic research and practical applications. The code structure typically involves image preprocessing, feature extraction, RBF network training using algorithms like k-means clustering for center selection, and output classification through weighted summation layers.