MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||10|
|Section||Modelling and Simulation|
|Published online||04 March 2020|
- Liu Xiao-dong, Life cycle cost analysis and control of equipment, National defence industry press, Beijing, 2008. [Google Scholar]
- Huang Xun-jiang, Life cycle cost evaluation and management of equipment with life cycle as independent variable, Science press, Beijing, 2012. [Google Scholar]
- General armament department of PLA, General requirements for materiel integrated logistics support:GJB3872-99, General armament department of PLA, Beijing, 1999. [Google Scholar]
- LIU Peng, DONG Zhen-qi, QU Yan, Life cycle cost analysis and optimization model of weapon and equipment system, Journal of Sichuan ordnance. 33(2012)57–59. [Google Scholar]
- LI Hong-tao. Application of life cycle cost theory in selection of speed control equipment for fan system, 2018’s china cement network annual conference. (2018):29–32. [Google Scholar]
- Meng Ke, ZHANG Bo. Analysis for life cycle cost of equipment using grey separating model, Fire control & command control. 36 (2011)79–84. [Google Scholar]
- ZHANG Xiao-hai, JIN Jia-shan, GENG Jun-bao, Modeling for life cycle cost by using DEA to optimizing principal components regression, Journal of naval university of engineering. 23(2011)32–36. [Google Scholar]
- LUO Wei, XIAO Shang-qin. Ship LCC based on improved SVM algorithm. Computer and digital engineering. 45(2017) 287–290. [Google Scholar]
- Robert Savic, Uwe K. Rakowsky. A neuro-fuzzy reliability optimization method considering life cycle costs, Probabilistic Safety Assessment and Management. (2004)1388–1394. [Google Scholar]
- LIANG Qing-wei, SONG Bao-wei, WU Chao-hui. Life cycle cost modeling of fuzzy least squares regression for weapon system, Fire control & command control. 31 (2006)45–47. [Google Scholar]
- CHEN Yong-hong, ZHANG Da-fa, CHEN Deng-ke, Prediction on corrosion rate of pipe in nuclear power system based on optimized grey theory, Atomic energy science and technology. 41(2007)707–710. [Google Scholar]
- CHEN Yong-hong, ZHANG Da-fa, WANG Yue-min. Prediction of pipeline corrosion rate based on grey markov models, Nuclear power engineering. 30(2009)95–98. [Google Scholar]
- XU Gang-nian, WANG You-zhi, WANG Shi-min, Main girder deformation prediction model with improved unequal interval weight gray correction, Journal of Sichuan university. 50(2018)91–99. [Google Scholar]
- Ma Guo-feng, Zhou Qiao-qiao, Research on adjustment model of PPP project concession period based on grey markov forecast, Science and technology management research. 38(2018)224–232. [Google Scholar]
- ZHANG Xi-Lai, ZHAO Jian-Hui, CAI Bo. Prediction model with dynamic adjustment for single time series of PM2. 5, Acta automatica sinica. 44(2018)1790–1798. [Google Scholar]
- CHEN De-yi, YAN Quan-sheng, Prediction research of special-shaped arch bridge arch-rib spatial alignment based on markov model, Journal of south china university of technology. 46(2018)41–47. [Google Scholar]
- ZHANG Jing-yi, LIAN Meng, GONG Jie, Combination forecasting of POL consumption based on time series and grey theory, Journal of ordnance equipment engineering. 39(2018)132–135. [Google Scholar]
- CHEN Jia-qi, SI Da-xiong, DING Lei, Application of DNGM (1, 1) prediction model in prediction of foundation pit water level change, Journal of Langfang normal university. 19 (2019)90–93. [Google Scholar]
- FU Ze-qiang, SUN Qi-hong, CAI Yun-long, Research on forecasting model of forest fire based on grey-system theory, Scientia silvae sinicae. 38(2002)95–100. [Google Scholar]
- LIN Yun, ZHANG Rong-hui, WANG Yue-neng. Modern statistical analysis methods, Zhejiang university press, Hangzhou, 1991. [Google Scholar]
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