Artículos de revistas
Storage and recall capabilities of fuzzy morphological associative memories with adjunction-based learning
Registro en:
Neural Networks. Pergamon-elsevier Science Ltd, v. 24, n. 1, n. 75, n. 90, 2011.
0893-6080
WOS:000289013500008
10.1016/j.neunet.2010.08.013
Autor
Valle, ME
Sussner, P
Institución
Resumen
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) We recently employed concepts of mathematical morphology to introduce fuzzy morphological associative memories (FMAMs), a broad class of fuzzy associative memories (FAMs). We observed that many well-known FAM models can be classified as belonging to the class of FMAMs. Moreover, we developed a general learning strategy for FMAMs using the concept of adjunction of mathematical morphology. In this paper, we describe the properties of FMAMs with adjunction-based learning. In particular, we characterize the recall phase of these models. Furthermore, we prove several theorems concerning the storage capacity, noise tolerance, fixed points, and convergence of auto-associative FMAMs. These theorems are corroborated by experimental results concerning the reconstruction of noisy images. Finally, we successfully employ FMAMs with adjunction-based learning in order to implement fuzzy rule-based systems in an application to a time-series prediction problem in industry. (C) 2010 Elsevier Ltd. All rights reserved. 24 1 75 90 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundacao Araucaria [14-1-15.197] Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) FAPESP [2006/06818-1] CNPq [306040/2006-9, 309608/2009-0] Fundacao Araucaria [14-1-15.197]