## Neural Networks for Signal Processing

Citations: | 3 - 0 self |

### BibTeX

@MISC{Svarer_neuralnetworks,

author = {Claus Svarer},

title = {Neural Networks for Signal Processing},

year = {}

}

### OpenURL

### Abstract

i Abstract In this thesis, methods for optimization of neural network architectures are examined in order to achieve better generalization ability from the neural networks at tasks within signal processing. The feed-forward networks described have one hidden layer of units with tanh activation functions and linear output units. The major topics described in the thesis are: ffl Reducing the number of free parameters in the network architecture by pruning of parameters. Pruning is based on estimates (Optimal Brain Damage) of which parameters induce the least increase in the network performance criterion (the costfunction) when they are removed from the network. ffl Finding methods for estimation of the generalization ability of the network from the learning data set. A generalization error estimate (Akaike's Final Prediction Error estimate) is used for choosing the optimal network architecture among different pruned network configurations. ffl Using methods for on-line tuning of the...