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A fast adaptive tunable RBF network for nonstationary systems

Chen, H., Gong, Y., Hong, X. ORCID: and Chen, S. (2016) A fast adaptive tunable RBF network for nonstationary systems. IEEE Transactions on Cybernetics, 46 (12). pp. 2683-2692. ISSN 2168-2267

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To link to this item DOI: 10.1109/TCYB.2015.2484378


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:65631

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