Noise effects in fuzzy modeling systems: Three case studies

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作者：

Lab. de Mecatrónica, Institute Superior Técnico, Av. Rovisco Pais, 1049-011 Lisboa Codex, Portugal ^{[1
]}

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来源：

Comput. Intell. Appl. | / 103-108期

关键词：

Approximation theory - Computational methods - Computer simulation - Functions - Gaussian noise (electronic) - Genetic algorithms - Learning algorithms - Noise pollution;

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摘要：

Noise is source of ambiguity for fuzzy systems. Although being an important aspect, the effects of noise in fuzzy modelling have been little investigated. This paper presents a set of tests using three well-known fuzzy modelling 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 modelling 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.

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