计算机工程 ›› 2012, Vol. 38 ›› Issue (08): 164-166.doi: 10.3969/j.issn.1000-3428.2012.08.054

• 人工智能及识别技术 • 上一篇    下一篇

改进在线贯序极限学习机在模式识别中的应用

尹 刚,张英堂,李志宁,范红波   

  1. (军械工程学院火炮工程系,石家庄 050003)
  • 收稿日期:2011-08-08 出版日期:2012-04-20 发布日期:2012-04-20
  • 作者简介:尹 刚(1988-),男,硕士研究生,主研方向:智能故障诊断;张英堂,教授、博士生导师;李志宁、范红波,讲师、博士

Application of Modified Online Sequential Extreme Learning Machine in Pattern Recognition

YIN Gang, ZHANG Ying-tang, LI Zhi-ning, FAN Hong-bo   

  1. (Department of Guns Engineering, Ordnance Engineering College, Shijiazhuang 050003, China)
  • Received:2011-08-08 Online:2012-04-20 Published:2012-04-20

摘要: 针对传统在线贯序极限学习机存在的过学习和分类器输出不稳定等问题,将结构风险最小化理论引入到极限学习机中,用小波函数替代原有的隐层激励函数构建正则小波极限学习机,并与在线学习方法结合,提出在线正则小波极限学习机。仿真实验结果表明,在线正则小波极限学习机克服过学习和局部最优等问题,能够实现快速在线学习,具有良好的泛化性和鲁棒性。

关键词: 在线贯序极限学习机, 小波分析, 在线学习, 模式识别, 结构风险, 泛化性能, 鲁棒性

Abstract: The traditional Online Sequential Extreme Learning Machine(OS-ELM) has variations in different trials of simulations and the over-learning problem. Using wavelet substitutes network’s traditional activation function, structural risk minimization is used to modify the problem, so a novel algorithm called regularized wavelet extreme learning machine is proposed. Motivated by online learning method, Online Sequential Regularized Wavelet Extreme Learning Machine(OS-RWELM) is designed. Experimental results show that this algorithm avoids the local minimum and over-learning problem, has a fast online learning speed and a good generalization and robustness.

Key words: Online Sequential Extreme Learning Machine(OS-ELM), wavelet analysis, online learning, pattern recognition, structural risk, generalization performance, robustness

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