THE LEVENBERG-MARQUARDT OPTIMIZATION AND THE OPTIMAL BRAIN SURGEON PRUNING
This paper presents the training, testing and pruning of a feedforward neural network with one hidden layer that was used for the prediction of the vowel ”a”. The paper also describes Gradient Descent, the Gauss-Newton and the Levenberg-Marquardt optimization techniques. Optimal Brain Surgeon pruning is applied to the trained network. The stopping criterion was an abrupt change of the Normalized Sum Squares Error. The structure of the feedforward neural network (FNN) was 18 inputs (four for glottal and 14 for speech samples), 3 neurons in the hidden layer and one output. The results have shown that, after pruning, the glottal signal has no effect on the model for a female speaker, while it affects the prediction of the speech pronounced by a male speaker. In both cases, the structure of the FNN is reduced to a small number of parameters.