Training Feedforward Neural Networks: Convergence and Robustness Analysis
Abstract
We develop a new algorithm for the learning of feedforward neural networks, by stating the learning process as a parameter estimation problem. We provide an analysis of its convegence and robustness properties. Two different versions of the algorithm are discussed, depending on the way in which the training set is explored during learning. The simulation results, for both classification and function approximation problems, confirm the...
Paper Details
Title
Training Feedforward Neural Networks: Convergence and Robustness Analysis
Published Date
Jan 1, 1999
Pages
267 - 272
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