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A fast adaptive tunable RBF network for nonstationary systems
journal contribution
posted on 2017-01-10, 10:03 authored by Hao Chen, Yu GongYu Gong, Xia Hong, Sheng ChenThis 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 CyberneticsVolume
46Issue
12Pages
2683-2692Citation
CHEN, H. ... et al., 2016. A fast adaptive tunable RBF network for nonstationary systems. IEEE Transactions on Cybernetics, 46 (12), 2683-2692.Publisher
© IEEEVersion
- 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
2015-09-20Publication date
2015-10-28Notes
© 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
2168-2267eISSN
2168-2275Publisher version
Language
- en