Forward and reverse modeling of electron beam welding process using radial basis function neural networks

An attempt has been made in the present study to model input-output relationships of an electron beam welding process in both forward as well as reverse directions using radial basis function neural networks. The performance of this network is dependent on its architecture significantly, which, in turn, depends on the number of hidden neurons, as the number of input nodes and that of output neurons can be decided beforehand for modeling a particular process. Input-output data can be clustered based on their similarity among them. The number of hidden neurons of this network is generally kept equal to that of clusters made by the data-set. Two popular fuzzy clustering algorithms, namely fuzzy C-means and entropy-based fuzzy clustering have been used for grouping the data into some clusters. As both these algorithms have inherent limitations, a modified clustering algorithm has been proposed by eliminating their demerits and combining their advantages. Radial basis function neural network developed using the proposed clustering algorithm is found to perform better than that designed based on the above two well-known clustering algorithms.