Implementation of RBF Neural Network
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Resource Overview
RBF neural network implementation with pre-loaded training and testing datasets, featuring customizable sample replacement for flexible model optimization
Detailed Documentation
In this project, we have successfully implemented a Radial Basis Function (RBF) neural network with pre-loaded training and testing datasets. The implementation utilizes Gaussian kernel functions as activation units in the hidden layer, employing clustering algorithms like K-means for center selection and gradient descent methods for weight optimization. The modular code architecture allows users to easily replace datasets according to specific requirements, enabling continuous model refinement. Through this work, we have established a deeper understanding of RBF network mechanics, including forward propagation calculations and parameter tuning strategies, providing a solid foundation for future research and practical applications in pattern recognition and function approximation tasks.
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