Veuillez utiliser cette adresse pour citer ce document : https://hdl.handle.net/10953/1783
Titre: Optimization algorithm for learning consistent belief rule-base from examples
Auteur(s): Liu, Jun
Martínez, Luis
Ruan, Da
Rodríguez, Rosa M.
Calzada, Alberto
Résumé: A belief rule-based inference approach and its corresponding optimization algorithm deal with a rule-base with a belief structure called a belief rule base (BRB) that forms a basis in the inference mechanism. In this paper, a new learning method is proposed based on the given sample data for optimally generating a consistent BRB. The focus is given on the consistency of BRB knowing that the consistency conditions are often violated if the system is generated from real world data. The measurement of BRB inconsistency is incorporated in the objective function of the optimization algorithm. This process is formulated as a non-linear constraint optimization problem and solved using the optimization tool provided in MATLAB. A numerical example is demonstrated the effectiveness of the proposed algorithm.
Mots-clés: Belief rule base
Optimization
Consistency
Learning
Date de publication: oct-2011
metadata.dc.description.sponsorship: research projects TIN2009-08286 y P08-TIC-3548
Editeur: Springer Link
Référence bibliographique: J. Liu, L. Martinez, D. Ruan et al. Optimization algorithm for learning consistent belief rule-base from examples. J Glob Optim 51, 255–270 (2011)
Collection(s) :DI-Artículos

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