An effective way to generate neural network structures for function approximation
One still open question in the area of research of multi-layer feedforward neural networks is concerning the number of neurons in its hidden layer(s). Especially in real life applications, this problem is often solved by heuristic methods. In this work an effective way to dynamically determine the n...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 1994 |
| 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:2099/2445 |
| Acceso en línea: | https://hdl.handle.net/2099/2445 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks Xarxes neuronals (Informàtica) Classificació AMS::68 Computer science::68T Artificial intelligence |
| Sumario: | One still open question in the area of research of multi-layer feedforward neural networks is concerning the number of neurons in its hidden layer(s). Especially in real life applications, this problem is often solved by heuristic methods. In this work an effective way to dynamically determine the number of hidden units in a three-layer feedforward neural network for function approximation is proposed. |
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