Journal
SWARM AND EVOLUTIONARY COMPUTATION
Volume 1, Issue 4, Pages 223-235Publisher
ELSEVIER
DOI: 10.1016/j.swevo.2011.07.001
Keywords
Particle swarm optimization; Neural networks; MIG welding; Forward and reverse mappings; Clustering; Hybrid algorithm
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Particle swarm optimization technique has been used for tuning of neural networks utilized for carrying out both forward and reverse mappings of metal inert gas (MIG) welding process. Four approaches have been developed and their performances are compared to solve the said problems. The first and second approaches deal with tuning of multi-layer feed-forward neural network and radial basis function neural network, respectively. In the third and fourth approaches, a back-propagation algorithm has been used along with particle swarm optimization to tune radial basis function neural network. Moreover, in these two approaches, two different clustering algorithms have been utilized to decide the structure of the network. The performances of hybrid approaches (that is, the third and fourth approaches) are found to be better than that of the other two. (C) 2011 Elsevier B.V. All rights reserved.
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