Neuro-fuzzy modelling based on a deterministic annealing approach
International Journal of Applied Mathematics and Computer Science, Tome 15 (2005) no. 4, p. 561.

Voir la notice de l'article dans European Digital Mathematics Library

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.
Classification : 68T05, 82C32
Mots-clés : deterministic annealing, neural networks, rules extraction, fuzzy systems, neuro-fuzzy systems, prediction, learning algorithm, fuzzy inference system 
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     title = {Neuro-fuzzy modelling based on a deterministic annealing approach},
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Robert Czabański. Neuro-fuzzy modelling based on a deterministic annealing approach. International Journal of Applied Mathematics and Computer Science, Tome 15 (2005) no. 4, p. 561. https://geodesic-test.mathdoc.fr/item/IJAMCS_2005__15_4_207767/