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The document discusses Radial Basis Function (RBF) networks, detailing how hidden neurons use Gaussian functions to determine outputs based on input proximity to neuron centers. It also explains the Least Mean Square (LMS) algorithm for updating neural network weights by minimizing error through iterative adjustments. Observations highlight the relationship between input distance, spread effects on output, and the convergence behavior of weight updates in the LMS algorithm.
- Author
- siddhishbanerjee2
- Language
- EN