Noise Effects in Fuzzy Modelling Systems

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dc.creatorBranco, P. J. Costa
dc.creatorDente, J. A.
dc.date2000-09-30
dc.date.accessioned2026-07-07T03:16:36Z
dc.date.available2026-07-07T03:16:36Z
dc.descriptionNoise is source of ambiguity for fuzzy systems. Although being an important aspect, the effects of noise in fuzzy modeling have been little investigated. This paper presents a set of tests using three well-known fuzzy modeling algorithms. These evaluate perturbations in the extracted rule-bases caused by noise polluting the learning data, and the corresponding deformations in each learned functional relation. We present results to show: 1) how these fuzzy modeling systems deal with noise; 2) how the established fuzzy model structure influences noise sensitivity of each algorithm; and 3) whose characteristics of the learning algorithms are relevant to noise attenuation.
dc.description6 pages
dc.identifierhttps://arxiv.org/abs/cs/0010002
dc.identifierhttp://arxiv.org/abs/cs/0010002
dc.identifierIn: Computational Intelligence and Applications, pp. 103-108, World Scientific and Engineering Society Press, Danvers, USA, 1999
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30410
dc.subjectNeural and Evolutionary Computing
dc.subjectMachine Learning
dc.subjectI.2.6; I.5.1; I.5.2
dc.titleNoise Effects in Fuzzy Modelling Systems
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