自动化技术、电信技术 |
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增强操作工况识别可靠性的概率PLS-ELM方法 |
赵立杰1,2, 柴天佑2, 袁德成1, 刁晓坤1 |
1. 沈阳化工大学 信息工程学院, 辽宁 沈阳 110142; 2.东北大学
流程工业综合自动化国家重点实验室,辽宁 沈阳 110004 |
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Probabilistic partial least square based extreme learning machine to
enhance reliability of operating conditions recognition |
ZHAO Li-jie1,2, CHAI Tian-you2, YUAN De-cheng1, DIAO Xiao-kun1 |
1. College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110042, China;
2. State Key Laboratory of Integrated Automation for Process Industries, Northeastern University,
Shenyang 110004, China |
引用本文:
赵立杰, 柴天佑, 袁德成, 刁晓坤. 增强操作工况识别可靠性的概率PLS-ELM方法[J]. J4, 2013, 47(10): 1747-1752.
ZHAO Li-jie, CHAI Tian-you, YUAN De-cheng, DIAO Xiao-kun. Probabilistic partial least square based extreme learning machine to
enhance reliability of operating conditions recognition. J4, 2013, 47(10): 1747-1752.
链接本文:
http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2013.10.007
或
http://www.zjujournals.com/eng/CN/Y2013/V47/I10/1747
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