| Full text | |
| Author(s): |
Total Authors: 4
|
| Affiliation: | [1] Univ Estadual Campinas, Dept Appl Math, BR-13081970 Campinas, SP - Brazil
[2] Univ Publ Navarra, Pamplona 31006 - Spain
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON FUZZY SYSTEMS; v. 23, n. 2, p. 313-326, APR 2015. |
| Web of Science Citations: | 11 |
| Abstract | |
Most fuzzy associative memories (FAMs) in the literature correspond to neural networks with a single layer of weights that distributively contains the information on associations to be stored. The main applications of these types of associative memory can be found in fuzzy rule-based systems. In contrast, T-fuzzy associative memories (T-FAMs) represent parametrized fuzzy neural networks with a hidden layer and these FAM models extend (dual) S-FAMs and SM-FAMs based on fuzzy subsethood and similarity measures. In this paper, we provide theoretical results concerning the storage capacity and error correction capability of T-FAMs. In addition, we introduce a training algorithm for T-FAMs and we compare the error rates produced by T-FAMs and some well-known classifiers in some benchmark classification problems that are available on the internet. Finally, we apply T-FAMs to a problem of vision-based self-localization in mobile robotics. (AU) | |
| FAPESP's process: | 09/16284-2 - Estimation of Mappings between Lattices Using (Fuzzy) Neurocomputing for Pattern Recognition |
| Grantee: | Estevão Esmi Laureano |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 11/10014-3 - Methods of computational intelligence and image processing based on mathematical morphology and lattice algebra |
| Grantee: | Peter Sussner |
| Support Opportunities: | Regular Research Grants |