Fuzzy reasoning spiking neural P system for fault diagnosis

TitleFuzzy reasoning spiking neural P system for fault diagnosis
Publication TypeJournal Papers
Year of Publication2013
AuthorsPeng, H., Wang J., Pérez-Jiménez M. J., Wang H., Shao J., & Wang T.
Journal TitleInformation Sciences
Place PublishedAmsterdam (The Netherlands)
Date Published06/2013

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.

KeywordsFault diagnosis; P systems; Spiking neural P systems; Fuzzy knowledge representation; Fuzzy reasoning
Impact Factor



8/135 - Q1

ISSN Number0020-0255