Optimization of multi-layer artificial neural networks using delta values of hidden layers

Abstract

The number of hidden layers is crucial in multilayer artificial neural networks. In general, generalization power of the solution can be improved by increasing the number of layers. This paper presents a new method to determine the optimal architecture by using a pruning technique. The unimportant neurons are identified by using the delta values of hidden layers. The modified network contains fewer numbers of neurons in network and shows better generalization. Moreover, it has improved the speed relative to the back propagation training. The experiments have been done with number of test problems to verify the effectiveness of new approach.