<?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%">Jun Wang</style></author><author><style face="normal" font="default" size="100%">Peng Shi</style></author><author><style face="normal" font="default" size="100%">Hong Peng</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</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%">Weighted Fuzzy Spiking Neural P Systems</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Fuzzy Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6242397</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">21</style></edition><publisher><style face="normal" font="default" size="100%">IEEE Computational Intelligence Society</style></publisher><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">209-220</style></pages><abstract><style face="normal" font="default" size="100%">Spiking neural P systems (SN P systems) are a new class of computing models inspired by neurophysiological behavior of biological spiking neurons. In order to make SN P systems capable to represent and process fuzzy and uncertain knowledge, we propose a new class of spiking neural P systems in this paper, called weighted fuzzy spiking neural P systems (WFSN P systems). Some new elements, including fuzzy truth value, certain factor, weighted fuzzy logic, output weight, threshold, new firing rule and two types of neurons, are added to original definition of SN P systems, which allow WFSN P systems to adequately characterize the features of weighted fuzzy production rules in a fuzzy rule-based system. Furthermore, a weighted fuzzy backward reasoning algorithm based on WFSN P systems is developed, which can accomplish dynamic fuzzy reasoning of a rule-based systems more flexibly and intelligently. In addition, we compare the proposed WFSN P systems with other knowledge representation methods, such as fuzzy production rule, conceptual graph and Petri nets, to demonstrate the features or advantages of the proposed techniques.</style></abstract><custom1><style face="normal" font="default" size="100%">5.484</style></custom1><custom2><style face="normal" font="default" size="100%">1/115 - Q1</style></custom2></record></records></xml>