New aspects on extraction of fuzzy rules using neural networks
In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe the behaviour of a system. We have used Artificial Neural Netorks (ANN) with the {\it Backpropagation} algorithm, and a set of examples of the system. In this work, some modifications which allow to imp...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 1998 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3535 |
| Acceso en línea: | https://hdl.handle.net/2099/3535 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial neural netwoks Learning Fuzzy rules Semantic in classification prosseses ANN Backpropagation algorithm Intel·ligència artificial Lògica difusa Xarxes neuronals (Informàtica) Classificació AMS::68 Computer science::68T Artificial intelligence |
| Sumario: | In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe the behaviour of a system. We have used Artificial Neural Netorks (ANN) with the {\it Backpropagation} algorithm, and a set of examples of the system. In this work, some modifications which allow to improve the results, by means of an aptation or refinement of the variable labels in each rule, or the extraction of local rules using distributed ANN, are showed. An interesting application on the assignement of semantic to the classes obtained in a classification without previous classes process is also included. |
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