<?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%">Hong Peng</style></author><author><style face="normal" font="default" size="100%">Min Tu</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author><author><style face="normal" font="default" size="100%">Peng Shi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Signal Processing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">AFSN P systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Fault diagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Member Computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Particle swarm optimization algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Power systems</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ejournal.org.cn/Jweb_cje/EN/abstract/abstract9370.shtml</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Chinese Institute of Electronics</style></publisher><pub-location><style face="normal" font="default" size="100%">Beijing, China</style></pub-location><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">320-327</style></pages><abstract><style face="normal" font="default" size="100%">Download: PDF (1027 KB)   HTML (1 KB)  
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Abstract  
A new fault diagnosis method based on improved Adaptive fuzzy spiking neural P systems (in short, AFSN P systems) and Particle swarm optimization (PSO) algorithm is presented to improve the efficiency and accuracy of diagnosis for power systems in this paper. AFSN P systems are a novel kind of computing models with parallel computing and learning ability. Based on our previous works, this paper focuses on AFSN P systems inference algorithms and learning algorithms and builds the fault diagnosis model using improved AFSN P systems for diagnosing effectively. The process of diagnosis based on AFSN P systems is expressed by matrix successfully to improve the rate of diagnosis eminently. Furthermore, particle swarm optimization algorithm is introduced into the learning algorithm of AFSN P systems, thus the convergence speed of diagnosis has a big progress. An example of 4-node system is given to verify the effectiveness of this method. Compared with the existing methods, this method has faster diagnosis speed, higher accuracy and strong ability to adapt to the grid topology changes.</style></abstract><custom1><style face="normal" font="default" size="100%">0.319</style></custom1><custom2><style face="normal" font="default" size="100%">226/249 - Q4</style></custom2></record></records></xml>