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Title: Online modeling with tunable RBF network
Authors: Chen, Hao
Gong, Yu
Hong, Xia
Keywords: Multi-innovation recursive least square (MRLS)
Nonlinear
Nonstationary
Online modeling
Quantum particle swarm optimization (QPSO)
Radial basis function (RBF)
Issue Date: 2012
Publisher: © IEEE
Citation: CHEN, H., GONG, Y. and HONG, X., 2012. Online modeling with tunable RBF network. IEEE Transactions on Cybernetics, 43 (3), pp. 935-947.
Abstract: In this paper, we propose a novel online modeling algorithm for nonlinear and nonstationary systems using a radial basis function (RBF) neural network with a fixed number of hidden nodes. Each of the RBF basis functions has a tunable center vector and an adjustable diagonal covariance matrix. A multi-innovation recursive least square (MRLS) algorithm is applied to update the weights of RBF online, while the modeling performance is monitored. When the modeling residual of the RBF network becomes large in spite of the weight adaptation, a node identified as insignificant is replaced with a new node, for which the tunable center vector and diagonal covariance matrix are optimized using the quantum particle swarm optimization (QPSO) algorithm. The major contribution is to combine the MRLS weight adaptation and QPSO node structure optimization in an innovative way so that it can track well the local characteristic in the nonstationary system with a very sparse model. Simulation results show that the proposed algorithm has significantly better performance than existing approaches.
Description: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
Sponsor: This work was supported by the UK Engineering and Physical Sciences Research Council and DSTL under Grant EP/H012516/1.
Version: Accepted for publication
DOI: 10.1109/TSMCB.2012.2218804
URI: https://dspace.lboro.ac.uk/2134/25658
Publisher Link: http://dx.doi.org/10.1109/TSMCB.2012.2218804
ISSN: 2168-2267
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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