A splicing system based genetic algorithm is proposed to optimize dyrnamical radial basis function (RBF) neural network,which is used to extract valuable process information from input output data.The novel RBF network training technique includes the network structure into the set of function centers by compromising between the conflicting requirements of reducing prediction error and simultaneously decreasing model complexity.The effectiveness of the proposed method is illustrated through the development of dynamic models as a benchmark discrete example and a continuous stirred tank reactor by comparing with several different RBF network training methods.