<?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%">Hong Peng</style></author><author><style face="normal" font="default" size="100%">Jun Wang</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author><author><style face="normal" font="default" size="100%">Hao Wang</style></author><author><style face="normal" font="default" size="100%">Jie Shao</style></author><author><style face="normal" font="default" size="100%">Tao Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fuzzy reasoning spiking neural P system for fault diagnosis</style></title><secondary-title><style face="normal" font="default" size="100%">Information Sciences</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Fault diagnosis; P systems; Spiking neural P systems; Fuzzy knowledge representation; Fuzzy reasoning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">(http://dx.doi.org/10.1016/j.ins.2012.07.015)</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam (The Netherlands)</style></pub-location><volume><style face="normal" font="default" size="100%">235</style></volume><pages><style face="normal" font="default" size="100%">106–116</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><custom1><style face="normal" font="default" size="100%">3.893</style></custom1><custom2><style face="normal" font="default" size="100%">8/135 - Q1</style></custom2></record></records></xml>