文献类型：期刊文献

英文题名：A modified adaptive chaotic binary ant system and its application in chemical process fault diagnosis

作者：Wang, Ling[1]; Yu, Jinshou[1]

机构：[1] Research Institution of Automation, East China University of Science and Technology, 200237, Shanghai, China

年份：2006

卷号：4222 LNCS - II

起止页码：530

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20064410206772)

语种：英文

外文关键词：Chaos theory - Failure analysis - Feature extraction - Learning systems

摘要：Fault diagnosis is a small sample problem as fault data are absent in the real production process. To tackle it, Support Vector Machines (SVM) is adopted to diagnose the chemical process steady faults in this paper. Considering the high data dimensionality in the large-scaled chemical industry seriously spoil classification capability of SVM, a modified adaptive chaotic binary ant system (ACBAS) is proposed and combined with SVM for fault feature selection to remove the irrelevant variables and ensure SVM classifying correctly. Simulation results and comparisons of Tennessee Eastman Process show the developed ACBAS can find the essential fault feature variables effectively and exactly, and the SVM fault diagnosis method combined with ACBAS-based feature selection greatly improve the diagnosing performance as unnecessary variables are eliminated properly. ? Springer-Verlag Berlin Heidelberg 2006.