文章基本信息

标题：Online Learning Algorithm for LSSVM Based Modeling with Time-varying Kernels
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作者：Weijian Kong ; Jinliang Ding
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：18
页码：626-630
DOI：10.1016/j.ifacol.2018.09.354
语种：English
出版社：Elsevier
摘要：AbstractOnline learning based Least Squares Support Vector Machine (LSSVM) can address the modeling problems of a time-varying process, which has a few advantages such as low training time and good general. Nevertheless, many of online learning algorithms cannot adapt the kernel parameters for the time-varying characteristic, so the inferred LSSVM models are low-accuracy. An online learning algorithm with time-varying kernels is proposed to improve online training accuracy of LSSVM model. The kernel parameters are optimized along with time-varying process using updating samples data. To achieve reliable performance during online optimization, we propose a controllable metaheuristic algorithm that adopts a contracted particle swarm optimization with an elaborate chaotic operator. The proposed modeling approach is utilized in the energy efficiency prediction of the electrical smelting process, and the experimental results show that the proposed online learning algorithm can both improve the accuracy of LSSVM model and ensure low online training time.
关键词：KeywordsTime-varying processonline learningLSSVMonline optimizationcontrollable metaheuristic algorithm