PSO-based neural network optimization and its utilization in a boring machine

This paper presents a particle swarm optimization (PSO) technique in training a multi-layer feed-forward neural network (MFNN) which is used for a prediction model of diameter error in a boring machining. Compared to the back propagation (BP) algorithm, the present algorithm achieves better machining precision with a fewer number of iterations.