Extraction of fuzzy rules using deterministic annealing integrated with ε-insensitive learning

Robert Czabański

International Journal of Applied Mathematics and Computer Science (2006)

  • Volume: 16, Issue: 3, page 357-372
  • ISSN: 1641-876X

Abstract

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A new method of parameter estimation for an artificial neural network inference system based on a logical interpretation of fuzzy if-then rules (ANBLIR) is presented. The novelty of the learning algorithm consists in the application of a deterministic annealing method integrated with ε-insensitive learning. In order to decrease the computational burden of the learning procedure, a deterministic annealing method with a "freezing" phase and ε-insensitive learning by solving a system of linear inequalities are applied. This method yields an improved neuro-fuzzy modeling quality in the sense of an increase in the generalization ability and robustness to outliers. To show the advantages of the proposed algorithm, two examples of its application concerning benchmark problems of identification and prediction are considered.

How to cite

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Czabański, Robert. "Extraction of fuzzy rules using deterministic annealing integrated with ε-insensitive learning." International Journal of Applied Mathematics and Computer Science 16.3 (2006): 357-372. <http://eudml.org/doc/207799>.

@article{Czabański2006,
abstract = {A new method of parameter estimation for an artificial neural network inference system based on a logical interpretation of fuzzy if-then rules (ANBLIR) is presented. The novelty of the learning algorithm consists in the application of a deterministic annealing method integrated with ε-insensitive learning. In order to decrease the computational burden of the learning procedure, a deterministic annealing method with a "freezing" phase and ε-insensitive learning by solving a system of linear inequalities are applied. This method yields an improved neuro-fuzzy modeling quality in the sense of an increase in the generalization ability and robustness to outliers. To show the advantages of the proposed algorithm, two examples of its application concerning benchmark problems of identification and prediction are considered.},
author = {Czabański, Robert},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {deterministic annealing; neural networks; rules extraction; fuzzy systems; neuro-fuzzy systems; -insensitivelearning; neuro-fuzzy system; fuzzy system},
language = {eng},
number = {3},
pages = {357-372},
title = {Extraction of fuzzy rules using deterministic annealing integrated with ε-insensitive learning},
url = {http://eudml.org/doc/207799},
volume = {16},
year = {2006},
}

TY - JOUR
AU - Czabański, Robert
TI - Extraction of fuzzy rules using deterministic annealing integrated with ε-insensitive learning
JO - International Journal of Applied Mathematics and Computer Science
PY - 2006
VL - 16
IS - 3
SP - 357
EP - 372
AB - A new method of parameter estimation for an artificial neural network inference system based on a logical interpretation of fuzzy if-then rules (ANBLIR) is presented. The novelty of the learning algorithm consists in the application of a deterministic annealing method integrated with ε-insensitive learning. In order to decrease the computational burden of the learning procedure, a deterministic annealing method with a "freezing" phase and ε-insensitive learning by solving a system of linear inequalities are applied. This method yields an improved neuro-fuzzy modeling quality in the sense of an increase in the generalization ability and robustness to outliers. To show the advantages of the proposed algorithm, two examples of its application concerning benchmark problems of identification and prediction are considered.
LA - eng
KW - deterministic annealing; neural networks; rules extraction; fuzzy systems; neuro-fuzzy systems; -insensitivelearning; neuro-fuzzy system; fuzzy system
UR - http://eudml.org/doc/207799
ER -

References

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