An Accelerated PSO Based Self-organizing RBF Neural Network for Nonlinear System Identification and Modeling

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In this paper, an accelerated particle swarm optimization (APSO) based radial basis function neural network (RBFNN) is designed for nonlinear system modeling. In APSO-RBFNN, the center, width of hidden neurons, weights of output layer and network size are optimized by using the APSO method. Two nonlinear system modeling experiments are used to illustrate the effectiveness of the proposed method. The simulation results show that the proposed method has obtained good performance in terms of network size and estimation accuracy.