Artículo
Fuzzy reasoning spiking neural P system for fault diagnosis
Autor/es | Peng, Hong
Wang, Jun Pérez Jiménez, Mario de Jesús Wang, Hao Shao, Jie Wang, Tao |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2013 |
Fecha de depósito | 2018-10-31 |
Publicado en |
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Resumen | Spiking neural P systems (SN P systems) have been well established as a novel class of distributed
parallel computing models. Some features that SN P systems possess are attractive
to fault diagnosis. However, handling ... Spiking neural P systems (SN P systems) have been well established as a novel class of distributed parallel computing models. Some features that SN P systems possess are attractive to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required for many fault diagnosis applications. The lack of ability is a major problem of existing SN P systems when applying them to the fault diagnosis domain. Thus, we extend SN P systems by introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems). The FRSN P systems are particularly suitable to model fuzzy production rules in a fuzzy diagnosis knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning algorithm based on FRSN P systems is developed according to neuron’s dynamic firing mechanism. Besides, a practical example of transformer fault diagnosis is used to demonstrate the feasibility and effectiveness of the proposed FRSN P systems in fault diagnosis problem. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía |
Identificador del proyecto | TIN2009–13192
P08-TIC-04200 |
Cita | Peng, H., Wang, J., Pérez Jiménez, M.d.J., Wang, H., Shao, J. y Wang, T. (2013). Fuzzy reasoning spiking neural P system for fault diagnosis. Information Sciences, 235 (junio 2013), 106-116. |
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