| Texto completo | |
| Autor(es): |
dos Santos, Alex Santana
;
Valle, Marcos Eduardo
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | APPLIEDMATH; v. 3, n. 4, p. 30-pg., 2023-12-01. |
| Resumo | |
Max-C and min-D projection auto-associative fuzzy morphological memories (max-C and min-D PAFMMs) are two-layer feedforward fuzzy morphological neural networks designed to store and retrieve finite fuzzy sets. This paper addresses the main features of these auto-associative memories: unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and excellent tolerance to either dilative or erosive noise. Particular attention is given to the so-called Zadeh' PAFMM, which exhibits the most significant noise tolerance among the max-C and min-D PAFMMs besides performing no floating-point arithmetic operations. Computational experiments reveal that Zadeh's max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with a strong potential for face recognition tasks. (AU) | |
| Processo FAPESP: | 22/01831-2 - Redes neurais artificiais de valor hipercomplexo: além dos números complexos e quaternios |
| Beneficiário: | Marcos Eduardo Ribeiro Do Valle Mesquita |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 19/02278-2 - Morfologia Matemática e Redes Neurais Morfológicas para Dados Multi-valorados |
| Beneficiário: | Marcos Eduardo Ribeiro Do Valle Mesquita |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |