[1]QIAO Junfei,AN Ru,HAN Honggui.Design of self-organizing RBF neural network based on relative contribution index[J].CAAI Transactions on Intelligent Systems,2018,13(2):159-167.[doi:10.11992/tis.201608009]
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Design of self-organizing RBF neural network based on relative contribution index

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 2 Page number: 159-167 Column: 学术论文—人工智能基础 Public date: 2018-04-15
Title:
Design of self-organizing RBF neural network based on relative contribution index
Author(s):
QIAO Junfei1; 2;  AN Ru1; 2;  HAN Honggui1; 2
1. College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computation Intelligence and Intelligence System, Beijing 100124, China
Keywords:
RBF neural network; relative contribution index; improved LM algorithm; structure design; ammonia and nitrogen effluent parameters; convergence speed; prediction accuracy
CLC:
TP183
DOI:
10.11992/tis.201608009
Abstract:
A design method for a self-organizing RBF Neural Network based on the Relative Contribution index is proposed with the aim of performing the structural design and parameter optimization of the Radial Basis Function (RBF) neural network. First, a self-organizing RBF network design method based on the Relative Contribution (RC) index is proposed. The relative contribution of the output of the hidden layer to the network output was used in order to assess whether a node of the hidden layer corresponding to the RBF network was inserted or pruned. Additionally, the convergence of the adjustment process of the neural structure was proven. Secondly, the adjusted network parameters were updated by the improved Levenberg-Marquardt (LM) algorithm in order to reduce the training time and increase the convergence speed of the network. Finally, the proposed algorithm was used in the simulation of the nonlinear function, and the modeling of the ammonia and nitrogen sewage effluent parameters. The simulation results revealed that the structure and parameters of the RBF neural network could be adjusted adaptively and dynamically according to the object under investigation, and that they had excellent approximation ability and higher prediction accuracy.
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