一种基于Akaike信息准则的极限学习机

山东大学学报(工学版) ›› 2011, Vol. 41 ›› Issue (6): 7-11.

一种基于Akaike信息准则的极限学习机

尹建川1,2, 邹早建1,3, 徐锋1

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

收稿日期:2011-04-08 出版日期:2011-12-16 发布日期:2011-04-08
作者简介:尹建川(1974- ),男,山东日照人，副教授，博士,主要研究方向为智能计算,控制理论与应用.E-mail:yinjianchuan@gmail.com
基金资助:
国家自然科学基金资助项目(51061130548;50979060)

An improved extreme learning machine based on Akaike criterion

YIN Jian-chuan1,2, ZOU Zao-jian1,3, XU Feng1

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

Received:2011-04-08 Online:2011-12-16 Published:2011-04-08

摘要/Abstract

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。

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

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 much faster training speed and satisfactory accuracy.

Key words: extreme learning machine, Akaike information criterion, modified Gram-Schmidt algorithm, feedforward neural network

中图分类号:

TP183

尹建川1,2, 邹早建1,3, 徐锋1. 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.

YIN Jian-chuan1,2, ZOU Zao-jian1,3, XU Feng1. An improved extreme learning machine based on Akaike criterion[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(6): 7-11.

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