Abstract
Input-output relationships of metal inert gas welding process were determined in both forward and reverse directions, which are required in order to automate the same. Various types of neural networks, namely multi-layer feed-forward network, counter-propagation network and radial basis function network had been used for the said purpose. The networks were trained using back-propagation algorithm and/or genetic algorithm. Their performances were compared, and radial basis function network developed using the concept of clustering was found to perform better than other networks in terms of accuracy in prediction.
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