# Neuro-fuzzy modelling based on a deterministic annealing approach

International Journal of Applied Mathematics and Computer Science (2005)

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

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topCzabański, 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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