This study investigates whether feedforward neural networks with two hidden layers generalise better than those with one. In contrast to the existing literature, a method is proposed which allows these networks to be compared empirically on a hidden-node-by-hidden-node basis. This is applied to ten public domain function approximation datasets. Networks with two hidden layers were found to be better generalisers in nine of the ten cases, although the actual degree of improvement is case dependent. The proposed method can be used to rapidly determine whether it is worth considering two hidden layers for a given problem.
|Name||Communications in Computer and Information Sciences|
|Conference||EANN: International Conference on Engineering Applications of Neural Networks|
|Period||2/08/17 → …|
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-65172-9_24