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. 2015 Nov 4;16:366. doi: 10.1186/s12859-015-0768-9

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© Jayavelu et al. 2015

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 5 — The attenuation factor, delta(δ) and ISNCA convergence properties. The selection of attenuation factor, δ with iterations is presented for five different scenarios (a) and four other scenarios (b). The corresponding two lower panels shows the error convergence responses to the ISNCA algorithm with ROBNCA, for the different δ values. a The δ is fixed at 0.5 at iteration one (k = 1) in all scenarios and δ is varied (0.2, 0.5, 0.8, 1.0 and 1.2) at subsequent iterations, k >1. The δ (k = 1) = 0.5 and δ (k >1) = 1.0 gives the best error and smooth convergence. b) The δ is fixed at 1.0 at all iterations (k >1) in all scenarios and δ is varied (0.3, 0.5, 0.7 and 1.0) at first iteration. The delta values δ (k = 1) = 0.5 and δ (k >1) = 1.0 gives the best error and smooth convergence (red solid line)