| 误差渐减在线序列ELM算法 |
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| 引用本文： | 王宇,张文芳.误差渐减在线序列ELM算法[J].咸阳师范学院学报,2014(2):5-8. |
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| 作者姓名： | 王宇 张文芳 |
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| 作者单位： | 西北大学数学学院,陕西西安710169 |
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| 摘 要： | 提出一种既能改变网络结构又能不断添加新数据的学习算法，克服了在线序列ELM一旦开始学习便无法调整网络结构的困难。基于在线序列ELM算法的基本思想和理论及目前面临的难点，结合已存在ELM及改进算法，探寻适合在线序列ELM算法的网络结构调整策略。误差渐减在线序列ELM算法可以解决在线序列ELM目前面临的挑战，且所提算法理论实验方面均切实可行。
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| 关 键 词： | 在线序列 误差渐减 网络结构 |
Error Decrease Online Sequence Extreme Learning Machine |
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| WANG Yu,ZHANG Wen-fang.Error Decrease Online Sequence Extreme Learning Machine[J].Journal of Xianyang Normal University,2014(2):5-8. |
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| Authors: | WANG Yu ZHANG Wen-fang |
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| Institution: | (Department of Mathematics, Northwest University, Xi＇an 710169, Shaanxi,China) |
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| Abstract: | In this paper, a learning algorithm that can not only transform network structure but also add to new data is proposed, it not only learns online data hut also self-adapts network meanwhile. Solv- ing the challenge that Online Sequence Extreme Learning Machine（OS-ELM） could not change struc- ture once the learning process starts. So we find a strategy to adjust the framework of network for OS-ELM which base on theory and some improved algorithms on OS-ELM and Extreme Learning Ma- chine（ELM）. Error Decrease Online Sequence Extreme Learning Machine can deal with the difficulty that OS-ELM is facing, and the algorithm proposed is reasonable in theory and numerical value experi- ment. |
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| Keywords: | online sequence extreme learning machine network |
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