Neuro and neuro-fuzzy hierarchical structures comparison in FDI: a case study
In this paper a hierarchical structure of several artificial neural networks has been developed for fault isolation purposes. Two different approaches have been considered. The hierarchical structure is the same for both approaches, but one uses multi-layer feedforward artificial neural networks and...
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| Outros Autores: | , , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2020
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.21/10979 |
| País: | Portugal |
| Oai: | oai:repositorio.ipl.pt:10400.21/10979 |
| Resumo: | In this paper a hierarchical structure of several artificial neural networks has been developed for fault isolation purposes. Two different approaches have been considered. The hierarchical structure is the same for both approaches, but one uses multi-layer feedforward artificial neural networks and the other uses fuzzy neural networks. A result comparison between the two architectures will be presented. It is aimed to isolate multiple simultaneous abrupt and incipient faults from only single abrupt fault symptoms. A continuous binary distillation column has been used as test bed of the current approaches. |
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