Tuning of neural networks using particle swarm optimization to model MIG welding process

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.

► Tuning of neural networks is done using particle swarm optimization technique. ► Multi-layer feed-forward and radial basis function neural networks are used. ► Structure of the neural networks is decided through clustering. ► Tuned neural networks are used for input–output modeling of MIG welding process.