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Radial Basis Function Neural Networks (RBFNNs) are a type of neural network that combines elements of clustering and function approximation, making them powerful for both regression and classification tasks. RBFNNs can efficiently model non-linear relationships with fewer parameters than traditional multilayer perceptrons, thanks to their unique architecture that uses radial basis functions (typically Gaussian) in the hidden layer.
This structure allows RBFNNs to approximate functions and decision boundaries with high accuracy while maintaining a relatively simple network structure.
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enhancementThis PR modified some existing filesThis PR modified some existing files