Optimal design of radial basis function neural networks for fuzzy-rule extraction in high dimensional data

摘要

The design of an optimal radial basis function neural network (RBFNF) is not a straightforward procedure. In this paper we take advantage of the functional equivalence between RBFN and fuzzy inference systems to propose a novel efficient approach to RBFN design for fuzzy rule extraction, The method is based on advanced fuzzy clustering techniques. Solutions to practical problems are proposed. By combining these different solutions, a general methodology is derived. The efficiency of our method is demonstrated on challenging synthetic and real world data sets. (C) 2001 Pattern Recognition Society, Published by Elsevier Science Ltd. All rights reserved. [References: 40]