Identification of Nonstationary Time Series Based on SVM-HMM Method

摘要：

Nonstationary time series are occurring when the plant proceeds to an abnormal state or a transient situation from a normal state.So it is necessary to identify the type of fault during its early stages for the selection of appropriate operator actions to prevent a more severe situation.This paper proposes a new architecture for identification of the time series.It converts the output of support vector machine (SVM) into the form of posterior probability which is computed by the combined use of sigmoid function and Gauss model,it acts as a probability evaluator in the hidden states of hidden Markov models (HMM).Experiments show that the architecture is very effective.

关键词： nonstationary time series HMM SVM identification pattern recognition

作者: Qiang Shao Cheng Shao Qiang Shao Changjian Feng

作者单位: Institute of Advanced control technologyDalian university of TechnologyDalian Liaoning Province,Chin Department of Mechanical EngineeringUniversity of Dalian NationalitiesDalian,Liaoning Province ,Chin Department of Mechanical Engineering University of Dalian Nationalities Dalian,Liaoning Province ,Ch

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Service Operations and Logistics, and Informatics(IEEE/SOLI’2008)(IEEE服务运作、物流与信息年会)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-12（万方平台首次上网日期，不代表论文的发表时间）