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

被引:1
|
作者
Huang, Wei [1 ]
Wang, Jinsong [1 ]
机构
[1] Tianjin Univ Technol, Sch Comp & Commun Engn, Tianjin 300384, Peoples R China
基金
中国国家自然科学基金;
关键词
INPUT SPACE; IDENTIFICATION; ALGORITHMS; PARTITION;
D O I
10.1155/2013/745314
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页数:10
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