| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Londrina, Dept Math, BR-86055900 Londrina, PR - Brazil
[2] Univ Estadual Campinas, Dept Appl Math, BR-13083859 Campinas, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | NEURAL NETWORKS; v. 24, n. 1, p. 75-90, JAN 2011. |
| Web of Science Citations: | 27 |
| Abstract | |
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) | |
| FAPESP's process: | 06/06818-1 - A general class of fuzzy morphological associative memories |
| Grantee: | Marcos Eduardo Ribeiro Do Valle Mesquita |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |