<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luis Valencia-Cabrera</style></author><author><style face="normal" font="default" size="100%">Bosheng Song</style></author><author><style face="normal" font="default" size="100%">Luis F. Macías-Ramos</style></author><author><style face="normal" font="default" size="100%">Linqiang Pan</style></author><author><style face="normal" font="default" size="100%">Agustín Riscos-Núñez</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational efficiency of P systems with symport/antiport rules and membrane separation</style></title><secondary-title><style face="normal" font="default" size="100%">Thirteenth Brainstorming Week on Membrane Computing</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Proceedings of the Thirteenth Brainstorming Week on Membrane Computing, </style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cs.us.es/~marper/investigacion/proceedings-13th-BWMC.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Fénix Editora</style></publisher><pub-location><style face="normal" font="default" size="100%">Sevilla, España</style></pub-location><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">325-370</style></pages><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>