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Neuro-fuzzy system with learning tolerant to imprecision

✍ Scribed by Jacek M. Łęski

Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
270 KB
Volume
138
Category
Article
ISSN
0165-0114

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✦ Synopsis

In this paper, a new learning method tolerant to imprecision is introduced and used in neuro-fuzzy modeling. This method can be called -insensitive learning, where in order to ÿt the fuzzy model to real data, a weighted -insensitive loss function is used. The proposed method makes it possible to exclude an intrinsic inconsistency of neuro-fuzzy modeling, where zero-tolerance learning is used to obtain a fuzzy model tolerant to imprecision.

The -insensitive learning leads to a model with the minimal Vapnik-Chervonenkis dimension (complexity), which results in improving generalization ability of this system and its robustness to outliers. Finally, numerical examples are given to demonstrate the validity of the introduced method.

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