RBF-NN (Radial Basis Function Neural Network)
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Resource Overview
RBF-NN, Radial Basis Function Neural Network. This implementation has undergone comprehensive validation tests, demonstrating reliable performance with proper code structure and parameter configuration.
Detailed Documentation
This article discusses RBF-NN (Radial Basis Function Neural Network). After a series of rigorous validations, we can confirm its effectiveness and reliability. The implementation typically involves three key components: an input layer, a hidden layer with radial basis activation functions (commonly using Gaussian functions with adjustable centers and widths), and a linear output layer. The training process usually employs clustering algorithms like K-means for center initialization and gradient descent methods for weight optimization, ensuring stable convergence and accurate pattern recognition capabilities.
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