Neuro-fuzzy modelling based on a deterministic annealing approach

Robert Czabański

International Journal of Applied Mathematics and Computer Science (2005)

  • Volume: 15, Issue: 4, page 561-576
  • ISSN: 1641-876X

Abstract

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This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty of the learning algorithm consists in the application of a deterministic annealing optimization method. It leads to an improvement in the neuro-fuzzy modelling performance. To show the validity of the introduced method, two examples of application concerning chaotic time series prediction and system identification problems are provided.

How to cite

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Czabański, Robert. "Neuro-fuzzy modelling based on a deterministic annealing approach." International Journal of Applied Mathematics and Computer Science 15.4 (2005): 561-576. <http://eudml.org/doc/207767>.

@article{Czabański2005,
abstract = {This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty of the learning algorithm consists in the application of a deterministic annealing optimization method. It leads to an improvement in the neuro-fuzzy modelling performance. To show the validity of the introduced method, two examples of application concerning chaotic time series prediction and system identification problems are provided.},
author = {Czabański, Robert},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {deterministic annealing; neural networks; rules extraction; fuzzy systems; neuro-fuzzy systems; prediction; learning algorithm; fuzzy inference system},
language = {eng},
number = {4},
pages = {561-576},
title = {Neuro-fuzzy modelling based on a deterministic annealing approach},
url = {http://eudml.org/doc/207767},
volume = {15},
year = {2005},
}

TY - JOUR
AU - Czabański, Robert
TI - Neuro-fuzzy modelling based on a deterministic annealing approach
JO - International Journal of Applied Mathematics and Computer Science
PY - 2005
VL - 15
IS - 4
SP - 561
EP - 576
AB - This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty of the learning algorithm consists in the application of a deterministic annealing optimization method. It leads to an improvement in the neuro-fuzzy modelling performance. To show the validity of the introduced method, two examples of application concerning chaotic time series prediction and system identification problems are provided.
LA - eng
KW - deterministic annealing; neural networks; rules extraction; fuzzy systems; neuro-fuzzy systems; prediction; learning algorithm; fuzzy inference system
UR - http://eudml.org/doc/207767
ER -

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