Robust Pruning of RBF Network for Neural Tracking Control Systems

Author

Ni, Jie ; Song, Qing ; Grimble, M.J.

Author_Institution

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

fYear

2006

fDate

13-15 Dec. 2006

Firstpage

6331

Lastpage

6336

Abstract

It is difficult to determine the number of nodes that should be used in a neural network. An adaptive method is proposed whereby the initial select is based on the largest expected number and the algorithm then "prunes" the numbers. A robust backpropagation training algorithm is proposed for the online tuning of a radial basis function(RBF) network tracking control system. The structure of the RBF network controller is derived using a filtered error approach. The proposed pruning method in this paper begins with a relatively large network, and certain neural units of the RBF network are dropped by examining the estimation error increment. A complete convergence proof is provided in the presence of disturbances

Keywords

backpropagation; neurocontrollers; radial basis function networks; robust control; RBF network tracking control; backpropagation training algorithm; filtered error approach; neural network; neural tracking control; radial basis function; robust pruning; Backpropagation algorithms; Control systems; Convergence; Error correction; Multi-layer neural network; Multilayer perceptrons; Neural networks; Radial basis function networks; Robust control; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2006 45th IEEE Conference on

Conference_Location

San Diego, CA

Print_ISBN

1-4244-0171-2

Type

conf

DOI

10.1109/CDC.2006.377039

Filename

4177052