Modeling of Hidden Layer Architecture in Multilayer Artificial Neural Networks

Abstract

A generated solution for an artificial neural network (ANN) may result in complex computations of neural networks, deployment, and usage of trained networks due to its inappropriate architecture. Therefore, modeling the hidden layer architecture of artificial neural networks remains as a research challenge. This paper presents a solution to achieve the hidden layer architecture of artificial neural networks which is inspired by some facts of neuroplasticity. The proposed method has two phases. First, it determines the number of hidden layers for the best architecture and then removes unnecessary hidden neurons from the network to enhance the performance. Experimental results in several benchmark problems show that the modified network shows better generalization than the original network.