  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.
