《计算机应用研究》|Application Research of Computers

基于自适应自然梯度法的在线高斯过程建模

Online learning algorithm of Gaussian process based on adaptive nature gradient

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作者 申倩倩,孙宗海
机构 华南理工大学 自动化科学与工程学院,广州 510640
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文章编号 1001-3695(2011)01-0095-03
DOI 10.3969/j.issn.1001-3695.2011.01.025
摘要 为了满足在线建模算法的实时性要求,提出了在高斯过程的训练中使用自适应自然梯度法(ANG),即基于自适应自然梯度法的在线高斯过程回归建模算法。将此算法运用在Micky-Glass系统和连续搅拌反应釜(CSTR)模型的建立中,并与稀疏在线高斯过程算法进行比较。仿真结果表明此算法满足了非线性系统建模的实时性和精度的要求,同时克服了其他方法计算量很大、不符合在线算法的实时性要求的缺点。
关键词 在线高斯过程;建模;自适应自然梯度法;Micky-Glass 系统;CSTR建模
基金项目 国家自然科学基金资助项目(60704012,60574019);广东省自然科学基金资助项目(06300232);中央高校科研业务费资助项目(2009zm0161)
本文URL http://www.arocmag.com/article/1001-3695(2011)01-0095-03.html
英文标题 Online learning algorithm of Gaussian process based on adaptive nature gradient
作者英文名 SHEN Qian-qian, SUN Zong-hai
机构英文名 College of Automation Science & Engineering, South China University of Technology, Guangzhou 510640, China
英文摘要 In order to satisfy the online modeling algorithm’s request of real-time, this paper proposed the adaptive natural gradient method used in online Gaussian process training.The algorithm was named online learning algorithm of Gaussian process based on adaptive nature gradient.The algorithm was applied in Micky-Glass system and continuous stirred tank reactor(CSTR)modeling, and compared with the sparse online Gaussian processes algorithm.Obtained from the simulation results, this algorithm meets the real-time and accuracy requirements of nonlinear system modeling, and overcomes other online algorithms’ faults of needing much computation resource and not to accord with the requirement of real-time of online algorithm.
英文关键词 online Gaussian process; modeling; adaptive natural gradient; Micky-Glass system; CSTR modeling
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