An Adaptive-PSO-Based Self-Organizing RBF Neural Network

作者：Han, Hong-Gui^*; Lu, Wei; Hou, Ying; Qiao, Jun-Fei

来源：IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 104-117.

DOI：10.1109/TNNLS.2016.2616413

• Adaptive particle swarm optimization (APSO)
• nonlinear system modeling
• radial basis function neural networks (RBFNNs)
• self-organizing

In this paper, a self-organizing radial basis SORBF) neural network is designed to improve both accuracy and parsimony with the aid of adaptive particle swarm optimization (APSO). In the proposed APSO algorithm, to avoid being trapped into local optimal values, a nonlinear regressive function is developed to adjust the inertia weight. Furthermore, the APSO algorithm can optimize both the network size and the parameters of an RBF neural network simultaneously. As a result, the proposed APSO-SORBF neural network can effectively generate a network model with a compact structure and high accuracy. Moreover, the analysis of convergence is given to guarantee the successful application of the APSO-SORBF neural network. Finally, multiple numerical examples are presented to illustrate the effectiveness of the proposed APSO-SORBF neural network. The results demonstrate that the proposed method is more competitive in solving nonlinear problems than some other existing SORBF neural networks.

• 出版日期2018-1
• 单位北京工业大学