Self-organizing design of radial basis function neural network based on neuron characteristics

Aiming at the problem that the hidden layer structure of radial basis neural function (RBF) neural network is difficult to determine, this paper introduces a self-organizing design method of RBF neural network based on the characteristics of neurons. This method combines the activation activity, significance and correlation of neurons Combined design of RBF (ASC-RBF) neural network. Firstly, The network uses the activity of neurons to adaptively increase the hidden layer neurons, and combines with its significance and correlation to complete the adaptive replacement and merging of neurons. Furthermore, the self-organizing design of the neural network is completed and its compactness is improved. Then, a second-order algorithm is used to modify the network parameters to ensure the accuracly of the RBF network. In addition, a stability analysis is given for the network structure self-organization mechanism. Finally, in order to verify the effectiveness of the proposed ASC-RBF network, two benchmark nonlinear system modeling experiments and a water quality parameter prediction experiment in a wastewater treatment system are performed. The results demonstrate that compared with the existing self-organizing network, the ASC-RBF neural network has faster training speed and a more compact network structure while ensuring generalization performance. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.