Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
This paper discusses the application of fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference abil...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/108010 |
| Acceso en línea: | https://hdl.handle.net/11441/108010 https://doi.org/10.15837/ijccc.2014.6.1485 |
| Access Level: | acceso abierto |
| Palabra clave: | fuzzy reasoning spiking neural P system with trapezoidal fuzzy number Fuzzy reasoning Fault diagnosis trapezoidal fuzzy number linguistic term |
| Sumario: | This paper discusses the application of fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference ability of tFRSN P systems from classical reasoning to fuzzy reasoning. Some case studies show the effectiveness of the presented method. We also briefly draw comparisons between the presented method and several main fault diagnosis approaches from the perspectives of knowledge representation and inference process. |
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