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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/23648

Title: Fast adaptive tunable RBF network for nonstationary systems
Authors: Chen, Hao
Gong, Yu
Hong, Xia
Chen, Sheng
Keywords: Radial basis function (RBF)
Multi-innovation recursive least square (MRLS)
Nonlinear
Nonstationary
On-line identification
Issue Date: 2016
Publisher: © IEEE
Citation: CHEN, H. ... et al., 2016. Fast adaptive tunable RBF network for nonstationary systems. IEEE Transactions on Cybernetics, 46 (12), 2683-2692.
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.
Description: © 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.
Version: Accepted for publication
DOI: 10.1109/TCYB.2015.2484378
URI: https://dspace.lboro.ac.uk/2134/23648
Publisher Link: http://dx.doi.org/10.1109/TCYB.2015.2484378
ISSN: 2168-2267
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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