Noise Effects in Fuzzy Modelling Systems
Neural and Evolutionary Computing
2007-05-23 v1 Machine Learning
Abstract
Noise 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.
Keywords
Cite
@article{arxiv.cs/0010002,
title = {Noise Effects in Fuzzy Modelling Systems},
author = {P. J. Costa Branco and J. A. Dente},
journal= {arXiv preprint arXiv:cs/0010002},
year = {2007}
}
Comments
6 pages