Seven RBF Neural Networks: Source Code Collection with Implementation Approaches

This documentation presents the complete source code implementation for seven distinct Radial Basis Function (RBF) neural networks, each designed using different algorithmic approaches. The implementations include gradient-based optimization methods for training, Ordinary Least Squares (OLS) for weight calculation, various clustering techniques for center selection, k-means clustering algorithms for basis function placement, and specialized function approximation methods. These algorithms are engineered to enhance both the accuracy and performance of neural network predictive models through optimized parameter tuning and efficient architecture design. The implementations demonstrate practical coding techniques for handling network scalability and generalization capabilities, making them suitable for diverse application scenarios. Key programming aspects covered include: initialization strategies for network parameters, iterative optimization procedures for gradient methods, matrix operations for OLS implementations, centroid calculation algorithms for clustering approaches, and error minimization techniques for function approximation. These source codes serve as valuable references for researchers and developers interested in neural network design and implementation, providing comprehensive examples of different RBF network configurations and their corresponding algorithmic implementations. The code structure emphasizes modular design, allowing easy adaptation and extension for specific use cases while maintaining computational efficiency.