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

International Journal of Applied Mathematics and Computer Science (2006)

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

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topCzabański, 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 -

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