Training neural networks with PSO in dynamic environments

Supervised neural networks (NNs) have been successfully applied to solve classification problems. Various NN training algorithms were developed, including the particle swarm optimiser (PSO), which was proved to outperform the standard back propagation training algorithm on a selection of problems. It was, however, usually assumed that the decision boundaries do not change over time. Such assumption is often not valid for real life problems, and training algorithms have to be adapted to track the changing decision boundaries and detect new boundaries as they appear. Various dynamic versions of the PSO have already been developed, and this paper investigates the applicability of dynamic PSO to NN training in changing environments.