This paper proposes a structure-improving particle swarm optimization (SPSO) algorithm for training artificial neural network (ANN).The algorithm is successfully applied to pattern classification problems including Iris

ionosphere and breast cancer.By tuning the structure and connection weights of ANN simultaneously

the proposed algorithm generates optimized ANN with problem-matched capacity for processing classification information.By doing this

it also eliminates some ill effects introduced by redundant input features and the corresponding structure of ANN.Compared with BP and GA based training techniques

SPSO can improve the classification accuracy while speeding up the convergence process.Simulation results show that SPSO is a potentially robust learning algorithm and could be extended to real world applications.