Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 25, Issue 10, Pages 1793-1803Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2013.2295813
Keywords
Error correction (ErrCor); incremental design; Levenberg-Marquardt (LM) algorithm; radial basis function (RBF) networks
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Funding
- National Science Centre (NCN-Poland) [DEC- 2013/11/B/ST6/01337]
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This paper proposes an offline algorithm for incrementally constructing and training radial basis function (RBF) networks. In each iteration of the error correction (ErrCor) algorithm, one RBF unit is added to fit and then eliminate the highest peak (or lowest valley) in the error surface. This process is repeated until a desired error level is reached. Experimental results on real world data sets show that the ErrCor algorithm designs very compact RBF networks compared with the other investigated algorithms. Several benchmark tests such as the duplicate patterns test and the two spiral problem were applied to show the robustness of the ErrCor algorithm. The proposed ErrCor algorithm generates very compact networks. This compactness leads to greatly reduced computation times of trained networks.
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