| 一种基于Akaike信息准则的极限学习机 |
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| 引用本文： | 尹建川,邹早建,徐锋. 一种基于Akaike信息准则的极限学习机[J]. 山东大学学报(工学版), 2011, 41(6): 7-11 |
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| 作者姓名： | 尹建川 邹早建 徐锋 |
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| 作者单位： | 1. 上海交通大学船舶海洋与建筑工程学院, 上海 200240; 2. 大连海事大学航海学院, 辽宁 大连 116026;3.上海交通大学海洋工程国家重点实验室, 上海 200240 |
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| 基金项目： | 国家自然科学基金资助项目(51061130548;50979060) |
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| 摘 要： | 为了减小传统的极限学习机网络的规模及提高网络的泛化性能,利用Akaike信息准则作为学习的最优停止准则以选择合适的隐层节点数量,同时利用修正Gram Schmidt算法自动调整网络参数,提出改进的极限学习机网络构造算法。通过与传统极限学习机在通用标杆问题上的实验结果比较表明, 该改进的极限学习机具有更精简的网络结构和更快的学习速度,同时具有良好的学习精度。
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| 关 键 词： | 极限学习机 Akaike信息准则 修正Gram-Schmidt算法 前向神经网络 |
| 收稿时间： | 2011-04-08 |
An improved extreme learning machine based on Akaike criterion |
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| YIN Jian-chuan,,ZOU Zao-jian,,XU Feng. An improved extreme learning machine based on Akaike criterion[J]. Journal of Shandong University of Technology, 2011, 41(6): 7-11 |
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| Authors: | YIN Jian-chuan ZOU Zao-jian XU Feng |
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| Affiliation: | 1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China;2. Navigation College, Dalian Maritime University, Dalian 116026, China;3. State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China |
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| Abstract: | To reduce the dimension of a neural network and improve the generalization capability of the extreme learning machine(ELM) network,Akaike information criterion(AIC) was implemented to choose a suitable number of hidden units,and the modified Gram-Schmidt(MGS) method was also implemented to automatically adjust the network parameters.In comparison with the conventional ELM learning method on several commonly used regressor benchmark problems,the improved ELM algorithm could achieve a compact network with muc... |
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| Keywords: | extreme learning machine Akaike information criterion modified Gram-Schmidt algorithm feedforward neural network |
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