Storage and recall capabilities of fuzzy morphological associative memories with adjunction-based learning

Autor(es)

Valle, Marcos Eduardo ^[1] ; Sussner, Peter ^[2]

Afiliação do(s) autor(es)

^[1] Univ Londrina, Dept Math, BR-86055900 Londrina, PR - Brazil

^[2] Univ Estadual Campinas, Dept Appl Math, BR-13083859 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: NEURAL NETWORKS; v. 24, n. 1, p. 75-90, JAN 2011.
• Citações Web of Science: 27

Resumo

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. (AU)

• Processo FAPESP: 06/06818-1 - Uma classe geral de memórias associativas morfológicas nebulosas
• Beneficiário: Marcos Eduardo Ribeiro Do Valle Mesquita
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado