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A fast adaptive tunable RBF network for nonstationary systems

A fast adaptive tunable RBF network for nonstationary systems
A fast adaptive tunable RBF network for nonstationary systems
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.
2168-2267
2683-2692
Chen, Hao
a6d9bdfb-0a77-43c7-93ae-659fd9e1623c
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Chen, Hao
a6d9bdfb-0a77-43c7-93ae-659fd9e1623c
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Chen, Hao, Gong, Yu, Hong, Xia and Chen, Sheng (2016) A fast adaptive tunable RBF network for nonstationary systems. IEEE Transactions on Cybernetics, 46 (12), 2683-2692.

Record type: Article

Abstract

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.

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Accepted/In Press date: 20 September 2015
e-pub ahead of print date: 28 October 2015
Published date: December 2016
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 403019
URI: https://eprints.soton.ac.uk/id/eprint/403019
ISSN: 2168-2267
PURE UUID: 9de14fe0-0040-4a4b-b8ba-a56d7d2a9f29

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Date deposited: 22 Nov 2016 14:03
Last modified: 10 Oct 2017 16:38

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