Computational efficiency of P systems with symport/antiport rules and membrane separation

TitleComputational efficiency of P systems with symport/antiport rules and membrane separation
Publication TypeConference Contributions
Year of Publication2015
AuthorsValencia-Cabrera, L., Song B., Macías-Ramos L. F., Pan L., Riscos-Núñez A., & Pérez-Jiménez M. J.
Conference NameThirteenth Brainstorming Week on Membrane Computing
Volume TitleProceedings of the Thirteenth Brainstorming Week on Membrane Computing,
PublisherFénix Editora
Place PublishedSevilla, España
Date Published02/2015

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.