Impact of Newman-Watts Small-World approach on The Performance of Feed-Forward Artificial Neural Networks

In the Feed Forward Neural Network(FFANN), performance of the learning changes with training algorithm, structure of hidden layer and number of neurons within the layers. Therefore, many research are conducted to improve the performance of network, and different approaches are suggested to achieve it. In this study, we analyse the impact of Newman-Watts Small-World (SW) approach on the performance of the FFANN. The obtained results show that the values of output error and determination coefficient of the network by the Newman-Watts SW approach are the better than those of the regular network, leading to the fact that the performance of Newman-Watts network is better than the regular networks. In this way, similar to the Watts-Strogatz FFANNs, it is shown that the Newman-Watts FFANNs can be used to solve black-box problem of ANN studies in the future.

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