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meteras

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都是期刊论文:
(1)Condition time series prediction of electronic system based on optimized relevance vector machine;系统工程与电子技术,2013第9期
(2)Probabilistic Prediction Method for Aeroengine Performance Parameters Based on Combined Optimum Relevance Vector Machine;航空学报,2013第9期
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xgj2008best

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meteras: 金币+50, ★★★★★最佳答案 2013-10-28 07:26:12
Accession number:       
20134116842745
        Title:        Condition time series prediction of electronic system based on optimized relevance vector machine
        Authors:         Fan, Geng1 ; Ma, Deng-Wu1 ; Wu, Ming-Hui2 ; Meng, Shang2
        Author affiliation:        1 Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, China
                2 Department of Scientific Research, Naval Aeronautical and Astronautical University, Yantai 264001, China
        Corresponding author:         Fan, G. (meteras@163.com)
        Source title:        Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics
        Abbreviated source title:        Xi Tong Cheng Yu Dian Zi Ji Shu/Syst Eng Electron
        Volume:        35
        Issue:        9
        Issue date:        September 2013
        Publication year:        2013
        Pages:        2011-2015
        Language:        Chinese
        ISSN:         1001506X
        CODEN:         XGYDEM
        Document type:        Journal article (JA)
        Publisher:        Chinese Institute of Electronics, P.O. Box 165, Beijing, 100036, China
        Abstract:        A method based on optimal relevance vector machine (RVM) is proposed to solve the problem of electronic system condition time series prediction. Based on the phase space reconstruction of electronic system condition time series, the RVM regression model is established. A quantum-behaved particle swarm optimization (QPSO) algorithm is employed to realize automatic selection of the established model parameters, which adopts cross-validation error as the optimization objective function and takes the kernel parameter as the particle position in quantum space. Experimental results show that the proposed method has higher point prediction accuracy and can provide probabilistic predictions, which is conducive to determine the future health status of electronic systems more reliably.
        Number of references:        17
        Main heading:         Electronics engineering
        Controlled terms:         Forecasting  -  Particle swarm optimization (PSO)  -  Phase space methods  -  Regression analysis  -  Time series
        Uncontrolled terms:         Cross validation  -  Electronic systems  -  Quantum-behaved particle swarm optimization  -  Relevance Vector Machine  -  Time series prediction
        Classification code:         922.2 Mathematical Statistics -  921 Mathematics -  723 Computer Software, Data Handling and Applications -  718 Telephone Systems and Related Technologies; Line Communications -  717 Optical Communication -  716 Telecommunication; Radar, Radio and Television -  715 Electronic Equipment, General Purpose and Industrial -  714 Electronic Components and Tubes -  713 Electronic Circuits
        DOI:        10.3969/j.issn.1001-506X.2013.09.35
        Database:        Compendex
                Compilation and indexing terms, © 2013 Elsevier Inc.


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