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| Author(s): |
Estevão Esmi
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | Campinas, SP. |
| Institution: | Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica |
| Defense date: | 2014-04-30 |
| Examining board members: |
Peter Sussner;
Roberto Andreani;
Laécio Carvalho de Barros;
Sandra Aparecida Sandri;
Benjamín René Callejas Bedregal
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| Advisor: | Peter Sussner |
| Abstract | |
Most fuzzy associative memories in the literature correspond to neural networks with a single layer of weights that distributively contains the information about the associations to be stored. The main applications of these types of associative memory can be found in fuzzy rule-base systems. In contrast, we present in this thesis the class of T-fuzzy associative memories (T-FAMs) that represent fuzzy neural networks with two layers. Particular cases of T-FAMs, called (dual) S-FAMs and E-FAMs, are based on fuzzy subsethood and equivalence measures. We provide theoretical results concerning the storage capability and error correction capability of T-FAMs. Furthermore, we introduce a general training algorithm for T-FAM that is guaranteed to converge in a finite numbers of iterations. We also proposed another alternative training algorithm for a certain type of E-FAM that not only adjusts the parameters of the corresponding network but also automatically determines its topology. We compare the classification rates produced by T-FAMs with that ones of some well-known classifiers in several benchmark classification problems that are available on the internet. Finally, we successful apply T-FAM approach to a problem of vision-based selflocalization 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 |