Training neuro-fuzzy models using evolution based algorithms
The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter is...
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| Outros Autores: | , |
| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2013
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.1/2328 |
| País: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2328 |
| Resumo: | The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems. |
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