IEEE Trans Neural Netw Learn Syst. 2018 Jan;29(1):104-117. doi: 10.1109/TNNLS.2016.2616413. Epub 2016 Oct 24.
In this paper, a self-organizing radial basis function (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.
本文设计了一种自组织径向基函数（SORBF）神经网络，借助自适应粒子群优化（APSO）算法来提高准确性和简约性。在提出的 APSO 算法中，为避免陷入局部最优值，开发了一种非线性回归函数来调整惯性权重。此外，APSO 算法可以同时优化 RBF 神经网络的网络大小和参数。因此，所提出的 APSO-SORBF 神经网络可以有效地生成具有紧凑结构和高精度的网络模型。此外，还给出了收敛性分析，以保证 APSO-SORBF 神经网络的成功应用。最后，提出了多个数值示例来说明所提出的 APSO-SORBF 神经网络的有效性。结果表明，与其他一些现有的 SORBF 神经网络相比，该方法在解决非线性问题方面更具竞争力。
