Conditional fuzzy clustering in the design of radial basis function neural networks

1998 ◽  
Vol 9 (4) ◽  
pp. 601-612 ◽  
Author(s):  
W. Pedrycz
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Fuzzy Clustering ◽  
Basis Function ◽  
Radial Basis

scholarly journals Design of Polynomial Fuzzy Radial Basis Function Neural Networks Based on Nonsymmetric Fuzzy Clustering and Parallel Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Wei Huang ◽  
Jinsong Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Fuzzy Clustering ◽  
Basis Function ◽  
Parallel Optimization ◽  
Clustering Method ◽  
Fair Competition ◽  
Fuzzy Clustering Method ◽  
Radial Basis

We first propose a Parallel Space Search Algorithm (PSSA) and then introduce a design of Polynomial Fuzzy Radial Basis Function Neural Networks (PFRBFNN) based on Nonsymmetric Fuzzy Clustering Method (NSFCM) and PSSA. The PSSA is a parallel optimization algorithm realized by using Hierarchical Fair Competition strategy. NSFCM is essentially an improved fuzzy clustering method, and the good performance in the design of “conventional” Radial Basis Function Neural Networks (RBFNN) has been proven. In the design of PFRBFNN, NSFCM is used to design the premise part of PFRBFNN, while the consequence part is realized by means of weighted least square (WLS) method. Furthermore, HFC-PSSA is exploited here to optimize the proposed neural network. Experimental results demonstrate that the proposed neural network leads to better performance in comparison to some existing neurofuzzy models encountered in the literature.

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