Max-Min fuzzy neural networks for solving relational equations
The Relational Equations approach is one of the most usual ones for describing (Fuzzy) Systems and in most cases, it is the final expression for other descriptions. This is why the identification of Relational Equations from a set of examples has received considerable atention in the specialized lit...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 1994 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2099/2461 |
| Acesso em linha: | https://hdl.handle.net/2099/2461 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Fuzzy relational equations Max-min neural networks Sistemes autoorganitzatius Aprenentatge automàtic Intel·ligència artificial Bases de dades relacionals Classificació AMS::68 Computer science::68T Artificial intelligence |
| Resumo: | The Relational Equations approach is one of the most usual ones for describing (Fuzzy) Systems and in most cases, it is the final expression for other descriptions. This is why the identification of Relational Equations from a set of examples has received considerable atention in the specialized literature. This paper is devoted to this topic, more specifically to the topic of max-min neural networks for identification. Three methods of "learning" Fuzzy Systems are developed by combining the most desirable properties of two existing ones: Sayto-Mukaidono's technique and the so called "smoothed derivative" technique. |
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