A self-organizing fuzzy neural network based on a growing-and-pruning algorithm

  • Authors:

  • Honggui Han;Junfei Qiao

  • Affiliations:

  • College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China;College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China

  • Venue:
  • IEEE Transactions on Fuzzy Systems
  • Year:
  • 2010

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Abstract

A novel growing-and-pruning (GP) approach is proposed, which optimizes the structure of a fuzzy neural network (FNN). This GP-FNN is based on radial basis function neurons, which have center and width vectors. The structure-learning phase and the parameter-training phase are performed concurrently. The structure-learning approach relies on the sensitivity analysis of the output. A set of fuzzy rules can be inserted or reduced during the learning process. The parameter-training algorithm is implemented using a supervised gradient decent method. The convergence of the GP-FNN-learning process is also discussed in this paper. The proposed method effectively generates a fuzzy neural model with a highly accurate and compact structure. Simulation results demonstrate that the proposed GP-FNN has a self-organizing ability, which can determine the structure and parameters of the FNN automatically. The algorithm performs better than some other existing self-organizing FNN algorithms.