文章基本信息

标题：Bacterial Foraging Optimization Combined with Relevance Vector Machine with an Improved Kernel for Pressure Fluctuation of Hydroelectric Units
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作者：Wang, Liying ; Yang, Shaopu
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2013
卷号：8
期号：5
页码：1273-1278
DOI：10.4304/jcp.8.5.1273-1278
语种：English
出版社：Academy Publisher
摘要：The optimization of kernel parameters is an important step in the application of the Relevance Vector Machine (RVM) for many real-world problems. In this paper, firstly we have developed an improved anisotropic Gaussian kernel as the kernel function of the RVM model, whose parameters are optimized by Bacterial Foraging Optimization (BFO). Then the proposed method is applied to describing the pressure fluctuation characteristics of the draft tube of hydroelectric units of a hydropower station, through the comparison, the simulation results show the parameters of the improved anisotropic Gaussian kernel are well optimized using the BFO, and the acquired RVM model can precisely describe the pressure fluctuation characteristics of the draft tube, and the less training samples are required to establish the accurate RVM model implying that it is more sparse than its counterpart.
关键词：Bacterial Foraging Optimization;Relevance Vector Machine;Hydroelectric units;Pressure Fluctuation