Creation and Comparison of RBF, GRNN, and PNN Neural Networks

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Detailed explanation of the establishment, model training, testing, and normalization processes for three neural network architectures, with performance evaluation and comparative analysis of different network performances, including result comparison images

Detailed Documentation

This article provides a comprehensive description of the construction process, model training, testing, and normalization procedures for three neural network architectures. The implementation includes detailed code approaches for network initialization, parameter optimization, and data preprocessing using techniques like Min-Max scaling. Performance evaluation metrics such as MSE (Mean Squared Error) and classification accuracy are calculated through custom validation functions. A comparative analysis examines the computational efficiency and prediction accuracy of different networks, with algorithmic explanations of their distinct learning mechanisms. Experimental results are visualized through comparison charts generated using matplotlib plotting libraries, clearly demonstrating performance differences across networks.

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