Aiming at the structural design and parameter optimization problems of radial basis function(RBF) neural network, an adaptive quantum-behaved particle swarm optimization(AQPSO) algorithm is proposed. In order to realize the self-organization learning of RBF nerual network and improve the performance, the network size and parameters of RBF neural network are mapped to the spatial position of the particles firstly, and then the weight mean of best particle positions is defined to evaluate $L_{i,j