Loughborough University
Browse
lsgd.pdf (625.19 kB)

A fast adaptive tunable RBF network for nonstationary systems

Download (625.19 kB)
journal contribution
posted on 2017-01-10, 10:03 authored by Hao Chen, Yu GongYu Gong, Xia Hong, Sheng Chen
This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Cybernetics

Volume

46

Issue

12

Pages

2683-2692

Citation

CHEN, H. ... et al., 2016. A fast adaptive tunable RBF network for nonstationary systems. IEEE Transactions on Cybernetics, 46 (12), 2683-2692.

Publisher

© IEEE

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2015-09-20

Publication date

2015-10-28

Notes

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

2168-2267

eISSN

2168-2275

Language

  • en