Generating extended fuzzy basis function networks using hybrid algorithm

Authors:
Bin Ye;Chengzhi Zhu;Chuangxin Guo;Yijia Cao
Affiliations:
College of Electrical Engineering, National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China;College of Electrical Engineering, National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China;College of Electrical Engineering, National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China;College of Electrical Engineering, National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Year:
2005

Citing 3
Cited 1

Evolutionary computation: toward a new philosophy of machine intelligence

Evolutionary computation: toward a new philosophy of machine intelligence
Parameter Selection in Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Construction of fuzzy systems using least-squares method and genetic algorithm

Fuzzy Sets and Systems - Theme: Modeling and control

A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new kind of Evolutionary Fuzzy System (EFS) based on the Least Squares (LS) method and a hybrid learning algorithm: Adaptive Evolutionary-programming and Particle-swarm-optimization (AEPPSO). The structure of the Extended Fuzzy Basis Function Network (EFBFN) is firstly proposed, and the LS method is used to design it with presetting the widths of the hidden units in EFBFN. Then, to enhance the performance of the obtained EFBFN ulteriorly, a novel learning algorithm based on least squares and the hybrid of evolutionary programming and particle swarm optimization (AEPPSO) is proposed, in which we use EPPSO to tune the parameters of the premise part in EFBFN, and the LS algorithm to decide the consequent parameters in it simultaneously. In the simulation part, the proposed method is employed to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed.