{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:40:37Z","timestamp":1760218837572,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2014,9,26]],"date-time":"2014-09-26T00:00:00Z","timestamp":1411689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In electric power systems, power cable operation under normal conditions is very important. Various cable faults will happen in practical applications. Recognizing the cable faults correctly and in a timely manner is crucial. In this paper we propose a method that an annealed chaotic competitive learning network recognizes power cable types. The result shows a good performance using the support vector machine (SVM) and improved Particle Swarm Optimization (IPSO)-SVM method. The experimental result shows that the fault recognition accuracy reached was 96.2%, using 54 data samples. The network training time is about 0.032 second. The method can achieve cable fault classification effectively.<\/jats:p>","DOI":"10.3390\/a7040492","type":"journal-article","created":{"date-parts":[[2014,9,26]],"date-time":"2014-09-26T11:27:58Z","timestamp":1411730878000},"page":"492-509","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network"],"prefix":"10.3390","volume":"7","author":[{"given":"Xuebin","family":"Qin","sequence":"first","affiliation":[{"name":"College of Electrical and Control Engineering, Xi'an University of Science and Technology,  Xi'an 710054, China"}]},{"given":"Mei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi'an University of Science and Technology,  Xi'an 710054, China"}]},{"given":"Jzau-Sheng","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Information Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan"}]},{"given":"Xiaowei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi'an University of Science and Technology,  Xi'an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2014,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1007\/s12404-008-0106-1","article-title":"Online fault recognition of electric power cable in coal mine based on the minimum risk neural network","volume":"14","author":"Wang","year":"2008","journal-title":"J. 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