A fast pruning algorithm for an Efficient Adaptive Fuzzy Neural Network

Author

Du Juan ; Er Meng Joo

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore

fYear

2010

fDate

9-11 June 2010

Firstpage

1030

Lastpage

1035

Abstract

A fast pruning algorithm for an Efficient Adaptive Fuzzy Neural Network (EAFNN) is presented in this paper. An EAFNN is a Takagi-Sugeno-Kang (TSK) type fuzzy model which is functionally equivalent to the Ellipsoidal Basis Function (EBF) neural network. An EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology are used to remove the unneeded hidden units. Simulation works show the proposed pruning algorithm is very efficient. It can not only reduce the complexity of the network but also accelerate the learning speed.

Keywords

adaptive systems; decision trees; fuzzy neural nets; learning (artificial intelligence); Optimal Brain Surgeon technology; Takagi-Sugeno-Kang type fuzzy model; efficient adaptive fuzzy neural network; ellipsoidal basis function neural network; error reduction ratio method; fast pruning algorithm; Adaptive control; Adaptive systems; Automatic control; Equations; Erbium; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Programmable control; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation (ICCA), 2010 8th IEEE International Conference on

Conference_Location

Xiamen

ISSN

1948-3449

Print_ISBN

978-1-4244-5195-1

Electronic_ISBN

1948-3449

Type

conf

DOI

10.1109/ICCA.2010.5524417

Filename

5524417