An improved approximation approach incorporating particle swarm optimization and a priori information into neural networks

In this paper, an improved approach incorporating adaptive particle swarm optimization (APSO) and a priori information into feedforward neural networks for function approximation problem is proposed. It is well known that gradient-based learning algorithms such as backpropagation algorithm have good ability of local search, whereas PSO has good ability of global search. Therefore, in the improved approach, the APSO algorithm encoding the first-order derivative information of the approximated function is used to train network to near global minima. Then, with the connection weights produced by APSO, the network is trained with a modified gradient-based algorithm with magnified gradient function. The modified gradient-based algorithm can reduce input-to-output mapping sensitivity and lessen the chance of being trapped into local minima. By combining APSO with local search algorithm and considering a priori information, the improved approach has better approximation accuracy and convergence rate. Finally, simulation results are given to verify the efficiency and effectiveness of the proposed approach.